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  • Published: 24 October 2019

A scoping review of the literature on the current mental health status of physicians and physicians-in-training in North America

  • Mara Mihailescu   ORCID: orcid.org/0000-0001-6878-1024 1 &
  • Elena Neiterman 2  

BMC Public Health volume  19 , Article number:  1363 ( 2019 ) Cite this article

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This scoping review summarizes the existing literature regarding the mental health of physicians and physicians-in-training and explores what types of mental health concerns are discussed in the literature, what is their prevalence among physicians, what are the causes of mental health concerns in physicians, what effects mental health concerns have on physicians and their patients, what interventions can be used to address them, and what are the barriers to seeking and providing care for physicians. This review aims to improve the understanding of physicians’ mental health, identify gaps in research, and propose evidence-based solutions.

A scoping review of the literature was conducted using Arksey and O’Malley’s framework, which examined peer-reviewed articles published in English during 2008–2018 with a focus on North America. Data were summarized quantitatively and thematically.

A total of 91 articles meeting eligibility criteria were reviewed. Most of the literature was specific to burnout ( n  = 69), followed by depression and suicidal ideation ( n  = 28), psychological harm and distress ( n  = 9), wellbeing and wellness ( n  = 8), and general mental health ( n  = 3). The literature had a strong focus on interventions, but had less to say about barriers for seeking help and the effects of mental health concerns among physicians on patient care.

Conclusions

More research is needed to examine a broader variety of mental health concerns in physicians and to explore barriers to seeking care. The implication of poor physician mental health on patients should also be examined more closely. Finally, the reviewed literature lacks intersectional and longitudinal studies, as well as evaluations of interventions offered to improve mental wellbeing of physicians.

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The World Health Organization (WHO) defines mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community.” [ 41 ] One in four people worldwide are affected by mental health concerns [ 40 ]. Physicians are particularly vulnerable to experiencing mental illness due to the nature of their work, which is often stressful and characterized by shift work, irregular work hours, and a high pressure environment [ 1 , 21 , 31 ]. In North America, many physicians work in private practices with no access to formal institutional supports, which can result in higher instances of social isolation [ 13 , 27 ]. The literature on physicians’ mental health is growing, partly due to general concerns about mental wellbeing of health care workers and partly due to recognition that health care workers globally are dissatisfied with their work, which results in burnout and attrition from the workforce [ 31 , 34 ]. As a consequence, more efforts have been made globally to improve physicians’ mental health and wellness, which is known as “The Quadruple Aim.” [ 34 ] While the literature on mental health is flourishing, however, it has not been systematically summarized. This makes it challenging to identify what is being done to improve physicians’ wellbeing and which solutions are particularly promising [ 7 , 31 , 33 , 37 , 38 ]. The goal of our paper is to address this gap.

This paper explores what is known from the existing peer-reviewed literature about the mental health status of physicians and physicians-in-training in North America. Specifically, we examine (1) what types of mental health concerns among physicians are commonly discussed in the literature; (2) what are the reported causes of mental health concerns in physicians; (3) what are the effects that mental health concerns may have on physicians and their patients; (4) what solutions are proposed to improve mental health of physicians; and (5) what are the barriers to seeking and providing care to physicians with mental health concerns. Conducting this scoping review, our goal is to summarize the existing research, identifying the need for a subsequent systematic review of the literature in one or more areas under the study. We also hope to identify evidence-based interventions that can be utilized to improve physicians’ mental wellbeing and to suggest directions for future research [ 2 ]. Evidence-based interventions might have a positive impact on physicians and improve the quality of patient care they provide.

A scoping review of the academic literature on the mental health of physicians and physicians-in-training in North America was conducted using Arksey and O’Malley’s [ 2 ] methodological framework. Our review objectives and broad focus, including the general questions posed to conduct the review, lend themselves to a scoping review approach, which is suitable for the analysis of a broader range of study designs and methodologies [ 2 ]. Our goal was to map the existing research on this topic and identify knowledge gaps, without making any prior assumptions about the literature’s scope, range, and key findings [ 29 ].

Stage 1: identify the research question

Following the guidelines for scoping reviews [ 2 ], we developed a broad research question for our literature search, asking what does the academic literature tell about mental health issues among physicians, residents, and medical students in North America ? Burnout and other mental health concerns often begin in medical training and continue to worsen throughout the years of practice [ 31 ]. Recognizing that the study and practice of medicine plays a role in the emergence of mental health concerns, we focus on practicing physicians – general practitioners, specialists, and surgeons – and those who are still in training – residents and medical students. We narrowed down the focus of inquiry by asking the following sub-questions:

What types of mental health concerns among physicians are commonly discussed in the literature?

What are the reported causes of mental health problems in physicians and what solutions are available to improve the mental wellbeing of physicians?

What are the barriers to seeking and providing care to physicians suffering from mental health problems?

Stage 2: identify the relevant studies

We included in our review empirical papers published during January 2008–January 2018 in peer-reviewed journals. Our exclusive focus on peer-reviewed and empirical literature reflected our goal to develop an evidence-based platform for understanding mental health concerns in physicians. Since our focus was on prevalence of mental health concerns and promising practices available to physicians in North America, we excluded articles that were more than 10 years old, suspecting that they might be too outdated for our research interest. We also excluded papers that were not in English or outside the region of interest. Using combinations of keywords developed in consultation with a professional librarian (See Table  1 ), we searched databases PUBMed, SCOPUS, CINAHL, and PsychNET. We also screened reference lists of the papers that came up in our original search to ensure that we did not miss any relevant literature.

Stage 3: literature selection

Publications were imported into a reference manager and screened for eligibility. During initial abstract screening, 146 records were excluded for being out of scope, 75 records were excluded for being outside the region of interest, and 4 papers were excluded because they could not be retrieved. The remaining 91 papers were included into the review. Figure  1 summarizes the literature search and selection.

figure 1

PRISMA Flow Diagram

Stage 4: charting the data

A literature extraction tool was created in Microsoft Excel to record the author, date of publication, location, level of training, type of article (empirical, report, commentary), and topic. Both authors coded the data inductively, first independently reading five articles and generating themes from the data, then discussing our coding and developing a coding scheme that was subsequently applied to ten more papers. We then refined and finalized the coding scheme and used it to code the rest of the data. When faced with disagreements on narrowing down the themes, we discussed our reasoning and reached consensus.

Stage 5: collating, summarizing, and reporting the results

The data was summarized by frequency and type of publication, mental health topics, and level of training. The themes inductively derived from the data included (1) description of mental health concerns affecting physicians and physicians-in-training; (2) prevalence of mental health concerns among this population; (3) possible causes that can explain the emergence of mental health concerns; (4) solutions or interventions proposed to address mental health concerns; (5) effects of mental health concerns on physicians and on patient outcomes; and (6) barriers for seeking and providing help to physicians afflicted with mental health concerns. Each paper was coded based on its relevance to major theme(s) and, if warranted, secondary focus. Therefore, one paper could have been coded in more than one category. Upon analysis, we identified the gaps in the literature.

Characteristics of included literature

The initial search yielded 316 records of which 91 publications underwent full-text review and were included in our scoping review. Our analysis revealed that the publications appear to follow a trend of increase over the course of the last decade reflecting the growing interest in physicians’ mental health. More than half of the literature was published in the last 4 years included in the review, from 2014 to 2018 ( n  = 55), with most publications in 2016 ( n  = 18) (Fig.  2 ). The majority of papers ( n  = 36) focused on practicing physicians, followed by papers on residents ( n  = 22), medical students ( n  = 21), and those discussing medical professionals with different level of training ( n  = 12). The types of publications were mostly empirical ( n  = 71), of which 46 papers were quantitative. Furthermore, the vast majority of papers focused on the United States of America (USA) ( n  = 83), with less than 9% focusing on Canada ( n  = 8). The frequency of identified themes in the literature is broken down into prevalence of mental health concerns ( n  = 15), causes of mental health concerns ( n  = 18), effects of mental health concerns on physicians and patients ( n  = 12), solutions and interventions for mental health concerns ( n  = 46), and barriers to seeking and providing care for mental health concerns ( n  = 4) (Fig.  3 ).

figure 2

Number of sources by characteristics of included literature

figure 3

Frequency of themes in literature ( n  = 91)

Mental health concerns and their prevalence in the literature

In this thematic category ( n  = 15), we coded the papers discussing the prevalence of specific mental health concerns among physicians and those comparing physicians’ mental health to that of the general population. Most papers focused on burnout and stress ( n  = 69), which was followed by depression and suicidal ideation ( n  = 28), psychological harm and distress ( n  = 9), wellbeing and wellness ( n  = 8), and general mental health ( n  = 3) (Fig.  4 ). The literature also identified that, on average, burnout and mental health concerns affect 30–60% of all physicians and residents [ 4 , 5 , 8 , 9 , 15 , 25 , 26 ].

figure 4

Number of sources by mental health topic discussed ( n  = 91)

There was some overlap between the papers discussing burnout, depression, and suicidal ideation, suggesting that work-related stress may lead to the emergence of more serious mental health problems [ 3 , 12 , 21 ], as well as addiction and substance abuse [ 22 , 27 ]. Residency training was shown to produce the highest rates of burnout [ 4 , 8 , 19 ].

Causes of mental health concerns

Papers discussing the causes of mental health concerns in physicians formed the second largest thematic category ( n  = 18). Unbalanced schedules and increasing administrative work were defined as key factors in producing poor mental health among physicians [ 4 , 5 , 6 , 13 , 15 , 27 ]. Some papers also suggested that the nature of the medical profession itself – competitive culture and prioritizing others – can lead to the emergence of mental health concerns [ 23 , 27 ]. Indeed, focus on qualities such as rigidity, perfectionism, and excessive devotion to work during the admission into medical programs fosters the selection of students who may be particularly vulnerable to mental illness in the future [ 21 , 24 ]. The third cluster of factors affecting mental health stemmed from structural issues, such as pressure from the government and insurance, fragmentation of care, and budget cuts [ 13 , 15 , 18 ]. Work overload, lack of control over work environment, lack of balance between effort and reward, poor sense of community among staff, lack of fairness and transparency by decision makers, and dissonance between one’s personal values and work tasks are the key causes for mental health concerns among physicians [ 20 ]. Govardhan et al. conceptualized causes for mental illness as having a cyclical nature - depression leads to burnout and depersonalization, which leads to patient dissatisfaction, causing job dissatisfaction and more depression [ 19 ].

Effects of mental health concerns on physicians and patients

A relatively small proportion of papers (13%) discussed the effects of mental health concerns on physicians and patients. The literature prioritized the direct effect of mental health on physicians ( n  = 11) with only one paper focusing solely on the indirect effects physicians’ mental health may have on patients. Poor mental health in physicians was linked to decreased mental and physical health [ 3 , 14 , 15 ]. In addition, mental health concerns in physicians were associated with reduction in work hours and the number of patients seen, decrease in job satisfaction, early retirement, and problems in personal life [ 3 , 5 , 15 ]. Lu et al. found that poor mental health in physicians may result in increased medical errors and the provision of suboptimal care [ 25 ]. Thus physicians’ mental wellbeing is linked to the quality of care provided to patients [ 3 , 4 , 5 , 10 , 17 ].

Solutions and interventions

In this largest thematic category ( n  = 46) we coded the literature that offered solutions for improving mental health among physicians. We identified four major levels of interventions suggested in the literature. A sizeable proportion of literature discussed the interventions that can be broadly categorized as primary prevention of mental illness. These papers proposed to increase awareness of physicians’ mental health and to develop strategies that can help to prevent burnout from occurring in the first place [ 4 , 12 ]. Some literature also suggested programs that can help to increase resilience among physicians to withstand stress and burnout [ 9 , 20 , 27 ]. We considered the papers referring to the strategies targeting physicians currently suffering from poor mental health as tertiary prevention . This literature offered insights about mindfulness-based training and similar wellness programs that can increase self-awareness [ 16 , 18 , 27 ], as well as programs aiming to improve mental wellbeing by focusing on physical health [ 17 ].

While the aforementioned interventions target individual physicians, some literature proposed workplace/institutional interventions with primary focus on changing workplace policies and organizational culture [ 4 , 13 , 23 , 25 ]. Reducing hours spent at work and paperwork demands or developing guidelines for how long each patient is seen have been identified by some researchers as useful strategies for improving mental health [ 6 , 11 , 17 ]. Offering access to mental health services outside of one’s place of employment or training could reduce the fear of stigmatization at the workplace [ 5 , 12 ]. The proposals for cultural shift in medicine were mainly focused on promoting a less competitive culture, changing power dynamics between physicians and physicians-in-training, and improving wellbeing among medical students and residents. The literature also proposed that the medical profession needs to put more emphasis on supporting trainees, eliminating harassment, and building strong leadership [ 23 ]. Changing curriculum for medical students was considered a necessary step for the cultural shift [ 20 ]. Finally, while we only reviewed one paper that directly dealt with the governmental level of prevention, we felt that it necessitated its own sub-thematic category because it identified the link between government policy, such as health care reforms and budget cuts, and the services and care physicians can provide to their patients [ 13 ].

Barriers to seeking and providing care

Only four papers were summarized in this thematic category that explored what the literature says about barriers for seeking and providing care for physicians suffering from mental health concerns. Based on our analysis, we identified two levels of factors that can impact access to mental health care among physicians and physicians-in-training.

Individual level barriers stem from intrinsic barriers that individual physicians may experience, such as minimizing the illness [ 21 ], refusing to seek help or take part in wellness programs [ 14 ], and promoting the culture of stoicism [ 27 ] among physicians. Another barrier is stigma associated with having a mental illness. Although stigma might be experienced personally, literature suggests that acknowledging the existence of mental health concerns may have negative consequences for physicians, including loss of medical license, hospital privileges, or professional advancement [ 10 , 21 , 27 ].

Structural barriers refer to the lack of formal support for mental wellbeing [ 3 ], poor access to counselling [ 6 ], lack of promotion of available wellness programs [ 10 ], and cost of treatment. Lack of research that tests the efficacy of programs and interventions aiming to improve mental health of physicians makes it challenging to develop evidence-based programs that can be implemented at a wider scale [ 5 , 11 , 12 , 18 , 20 ].

Our analysis of the existing literature on mental health concerns in physicians and physicians-in-training in North America generated five thematic categories. Over half of the reviewed papers focused on proposing solutions, but only a few described programs that were empirically tested and proven to work. Less common were papers discussing causes for deterioration of mental health in physicians (20%) and prevalence of mental illness (16%). The literature on the effects of mental health concerns on physicians and patients (13%) focused predominantly on physicians with only a few linking physicians’ poor mental health to medical errors and decreased patient satisfaction [ 3 , 4 , 16 , 24 ]. We found that the focus on barriers for seeking and receiving help for mental health concerns (4%) was least prevalent. The topic of burnout dominated the literature (76%). It seems that the nature of physicians’ work fosters the environment that causes poor mental health [ 1 , 21 , 31 ].

While emphasis on burnout is certainly warranted, it might take away the attention paid to other mental health concerns that carry more stigma, such as depression or anxiety. Establishing a more explicit focus on other mental health concerns might promote awareness of these problems in physicians and reduce the fear such diagnosis may have for doctors’ job security [ 10 ]. On the other hand, utilizing the popularity and non-stigmatizing image of “burnout” might be instrumental in developing interventions promoting mental wellbeing among a broad range of physicians and physicians-in-training.

Table  2 summarizes the key findings from the reviewed literature that are important for our understanding of physician mental health. In order to explicitly summarize the gaps in the literature, we mapped them alongside the areas that have been relatively well studied. We found that although non-empirical papers discussed physicians’ mental wellbeing broadly, most empirical papers focused on medical specialty (e.g. neurosurgeons, family medicine, etc.) [ 4 , 8 , 15 , 19 , 25 , 28 , 35 , 36 ]. Exclusive focus on professional specialty is justified if it features a unique context for generation of mental health concerns, but it limits the ability to generalize the findings to a broader population of physicians. Also, while some papers examined the impact of gender on mental health [ 7 , 32 , 39 ], only one paper considered ethnicity as a potential factor for mental health concerns and found no association [ 4 ]. Given that mental health in the general population varies by gender, ethnicity, age, and sexual orientation, it would be prudent to examine mental health among physicians using an intersectional analysis [ 30 , 32 , 39 ]. Finally, of the empirical studies we reviewed, all but one had a cross-sectional design. Longitudinal design might offer a better understanding of the emergence and development of mental health concerns in physicians and tailor interventions to different stages of professional career. Additionally, it could provide an opportunity to evaluate programs’ and policies’ effectiveness in improving physicians’ mental health. This would also help to address the gap that we identified in the literature – an overarching focus on proposing solutions with little demonstrated evidence they actually work.

This review has several limitations. First, our focus on academic literature may have resulted in overlooking the papers that are not peer-reviewed but may provide interesting solutions to physician mental health concerns. It is possible that grey literature – reports and analyses published by government and professional organizations – offers possible solutions that we did not include in our analysis or offers a different view on physicians’ mental health. Additionally, older papers and papers not published in English may have information or interesting solutions that we did not include in our review. Second, although our findings suggest that the theme of burnout dominated the literature, this may be the result of the search criteria we employed. Third, following the scoping review methodology [ 2 ], we did not assess the quality of the papers, focusing instead on the overview of the literature. Finally, our research was restricted to North America, specifically Canada and the USA. We excluded Mexico because we believed that compared to the context of medical practice in Canada and the USA, which have some similarities, the work experiences of Mexican physicians might be different and the proposed solutions might not be readily applicable to the context of practice in Canada and the USA. However, it is important to note that differences in organization of medical practice in Canada and the USA do exist, as do differences across and within provinces in Canada and the USA. A comparative analysis can shed light on how the structure and organization of medical practice shapes the emergence of mental health concerns.

The scoping review we conducted contributes to the existing research on mental wellbeing of American and Canadian physicians by summarizing key knowledge areas and identifying key gaps and directions for future research. While the papers reviewed in our analysis focused on North America, we believe that they might be applicable to the global medical workforce. Identifying key gaps in our knowledge, we are calling for further research on these topics, including examination of medical training curricula and its impact on mental wellbeing of medical students and residents, research on common mental health concerns such as depression or anxiety, studies utilizing intersectional and longitudinal approaches, and program evaluations assessing the effectiveness of interventions aiming to improve mental wellbeing of physicians. Focus on the effect physicians’ mental health may have on the quality of care provided to patients might facilitate support from government and policy makers. We believe that large-scale interventions that are proven to work effectively can utilize an upstream approach for improving the mental health of physicians and physicians-in-training.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

United States of America

World Health Organization

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M.M. and E.N. were involved in identifying the relevant research question and developing the combinations of keywords used in consultation with a professional librarian. M.M. performed the literature selection and screening of references for eligibility. Both authors were involved in the creation of the literature extraction tool in Excel. Both authors coded the data inductively, first independently reading five articles and generating themes from the data, then discussing their coding and developing a coding scheme that was subsequently applied to ten more papers. Both authors then refined and finalized the coding scheme and M.M. used it to code the rest of the data. M.M. conceptualized and wrote the first copy of the manuscript, followed by extensive drafting by both authors. E.N. was a contributor to writing the final manuscript. All authors read and approved the final manuscript.

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Mihailescu, M., Neiterman, E. A scoping review of the literature on the current mental health status of physicians and physicians-in-training in North America. BMC Public Health 19 , 1363 (2019). https://doi.org/10.1186/s12889-019-7661-9

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Cognitive–behavioral therapy (CBT) helps individuals to eliminate avoidant and safety-seeking behaviors that prevent self-correction of faulty beliefs, thereby facilitating stress management to reduce stress-related disorders and enhance mental health. The present review evaluated the effectiveness of CBT in stressful conditions among clinical and general populations, and identified recent advances in CBT-related techniques. A search of the literature for studies conducted during 1987–2021 identified 345 articles relating to biopsychosocial medicine; 154 (45%) were review articles, including 14 systemic reviews, and 53 (15%) were clinical trials including 45 randomized controlled trials. The results of several randomized controlled trials indicated that CBT was effective for a variety of mental problems (e.g., anxiety disorder, attention deficit hypersensitivity disorder, bulimia nervosa, depression, hypochondriasis), physical conditions (e.g., chronic fatigue syndrome, fibromyalgia, irritable bowel syndrome, breast cancer), and behavioral problems (e.g., antisocial behaviors, drug abuse, gambling, overweight, smoking), at least in the short term; more follow-up observations are needed to assess the long-term effects of CBT. Mental and physical problems can likely be managed effectively with online CBT or self-help CBT using a mobile app, but these should be applied with care, considering their cost-effectiveness and applicability to a given population.

History of cognitive–behavioral therapy (CBT)

CBT is a type of psychotherapeutic treatment that helps people to identify and change destructive or disturbing thought patterns that have a negative influence on their behavior and emotions [ 1 ]. Under stressful conditions, some individuals tend to feel pessimistic and unable to solve problems. CBT promotes more balanced thinking to improve the ability to cope with stress. The origins of CBT can be traced to the application of learning theory principles, such as classical and operant conditioning, to clinical problems. So-called “first-wave” behavioral therapy was developed in the 1950s [ 2 ]. In the US, Albert Ellis founded rational emotive therapy to help clients modify their irrational thoughts when encountering problematic events, and Aaron Beck employed cognitive therapy for depressed clients using Ellison’s model [ 3 ]. Behavioral therapy and cognitive therapy were later integrated in terms of theory and practice, leading to the emergence of “second-wave” CBT in the 1960s. The first- and second-wave forms of CBT arose via attempts to develop well-specified and rigorous techniques based on empirically validated basic principles [ 4 ]. From the 1960s onward, the dominant psychotherapies worldwide have been second-wave forms of CBT. Recently, however, a third-wave form of CBT has attracted increasing attention, leading to new treatment approaches such as acceptance and commitment therapy, dialectical behavior therapy, mindfulness-based cognitive therapy, functional analytic psychotherapy, and extended behavioral activation; other forms may also exist, although this is subject to conjecture [ 4 ]. In a field of psychosomatic medicine, it has been reported that cognitive restructuring is effective in improving psychosomatic symptoms [ 5 ], exposure therapy is suitable for a variety of anxious disease conditions like panic disorder and agoraphobia [ 6 ], and mindfulness reduces stress-related pain in fibromyalgia [ 7 ]. Several online and personal computer-based CBT programs have also been developed, with or without the support of clinicians; these can also be accessed by tablets or smartphones [ 8 ]. Against this background, this review focused on the effectiveness of CBT with a biopsychosocial approach, and proposed strategies to promote CBT application to both patient and non-patient populations.

Research on CBT

Using “CBT “and “biopsychosocial” as PubMed search terms, 345 studies published between January 1987 and May 2021 were identified (Fig.  1 ); 14 of 154 review articles were systemic reviews, and 45 of 53 clinical trials were randomized controlled trials. Most clinical trials recruited the samples from patient populations in order to assess specific diseases, but some targeted at those from non-patient populations like a working population in order to assessing mind-body conditions relating to sick leave [ 9 ]. The use of biopsychosocial approaches to treat chronic pain is shown to be clinically and economically efficacious [ 10 ]; for example, CBT is effective for chronic low-back pain [ 11 ]. The prevalence of chronic low-back pain, defined as pain lasting for more than 3 months, was reported to be 9% in primary-care settings and 7–29% in community settings [ 12 ]. Chronic low-back pain is not only prevalent, but is a source of significant physical disability, role impairment, and diminished psychological well-being and quality of life [ 11 ]. Interestingly, according to the results of our own study [ 13 ], CBT was effective among hypochondriacal patients without chronic low-back pain, but not in hypochondriacal patients with chronic low-back pain. These group differences did not seem to be due to differences in the baseline levels of hypochondriasis. Although evidence has suggested that both hypochondriasis and chronic low-back pain can be treated effectively with CBT [ 10 , 11 , 14 ], this has not yet been validated. Chronic low-back pain may be associated with a variety of conditions, including anxiety, depression, and somatic disorders such as illness conviction, disease phobia, and bodily preoccupation. The core psychopathology of hypochondriacal chronic low-back pain should be clarified to promote adequate symptom management [ 13 ].

figure 1

Number of articles per year identified by a PubMed search from 1989 to the present

Since 2000, Cochrane reviews have evaluated the effectiveness of CBT for a variety of mental, physical, and behavioral problems. Through a search of the Cochrane Library database up to May 2021 [ 15 ], 124 disease conditions were assessed to clarify the effects of CBT in randomized controlled trials; the major conditions for which CBT showed efficacy are listed in Table  1 . These include a broad range of medical problems such as psychosomatic illnesses (e.g., chronic fatigue syndrome, irritable bowel syndrome, and fibromyalgia), psychiatric disorders (e.g., anxiety, depression, and developmental disability), and socio-behavioral problems (drug abuse, smoking, and problem gambling). For most of these conditions, CBT proved effective in the short term after completion of the randomized controlled trial. Although the number of literature was still limited, some studies have reported significant and long-term treatment effects of CBT on some aspects of mental health like obsessive-compulsive disorder [ 16 ] 1 year after the completion of intervention. Future research should investigate the duration of CBT’s effects and ascertain the optimal treatment intensity, including the number of sessions.

Future directions for CBT application in biopsychosocial domains

In Japan, CBT for mood disorders was first covered under the National Health Insurance (NHI) in 2010, and CBT for the following psychiatric disorders was subsequently added to the NHI scheme: obsessive–compulsive disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, and bulimia nervosa [ 17 ]. The treatment outcomes and health insurance costs for these six disorders should be analyzed as the first step, for appropriate allocation of medical resources according to disease severity and complexity [ 18 ]. In Japan, health insurance coverage is provided only when physicians apply for remuneration. A system promoting nurse involvement in CBT delivery [ 19 ], as well as shared responsibility between the CBT instructor and certified psychologists (or even a complete shift from physicians to psychologists), has yet to be established. Information and communication technology (ICT) devices may allow CBT delivery to be shared between medical staff and psychologists, in medical, community and self-help settings [ 8 ]. The journal BioPsychoSocial Medicine published 334 relevant articles up to the end of May 2021, 112 (33.5%) of which specifically addressed CBT [ 20 ]. CBT is a hot topic in biopsychosocial medicine, and more research is required to encourage its application to clinical and general populations.

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Abbreviations

  • Cognitive–behavioral therapy

Information and communication technology

National Health Insurance

Post-traumatic stress disorder

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Acknowledgments

The study was supported in part by a Research Grant (Kiban C) from the Japanese Ministry of Education, Culture, Sports, Science and Technology.

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Nakao, M., Shirotsuki, K. & Sugaya, N. Cognitive–behavioral therapy for management of mental health and stress-related disorders: Recent advances in techniques and technologies. BioPsychoSocial Med 15 , 16 (2021). https://doi.org/10.1186/s13030-021-00219-w

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Stress, Anxiety, and Depression Among Undergraduate Students during the COVID-19 Pandemic and their Use of Mental Health Services

Jungmin lee.

1 Department of Educational Policy Studies and Evaluation, University of Kentucky, 597 S. Upper Street, 131 Taylor Education Building, Lexington, KY 40506-0001 USA

Hyun Ju Jeong

2 Department of Integrated Strategic Communication, University of Kentucky, Lexington, KY USA

3 Division of Biomedical Informatics, University of Kentucky, Lexington, KY USA

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The coronavirus 2019 (COVID-19) has brought significant changes to college students, but there is a lack of empirical studies regarding how the pandemic has affected student mental health among college students in the U.S. To fill the gap in the literature, this study describes stress, anxiety, and depression symptoms for students in a public research university in Kentucky during an early phase of COVID-19 and their usage of mental health services. Results show that about 88% of students experienced moderate to severe stress, with 44% of students showing moderate to severe anxiety and 36% of students having moderate to severe depression. In particular, female, rural, low-income, and academically underperforming students were more vulnerable to these mental health issues. However, a majority of students with moderate or severe mental health symptoms never used mental health services. Our results call for proactively reaching out to students, identifying students at risk of mental health issues, and providing accessible care.

The coronavirus 2019 (COVID-19) has brought significant and sudden changes to college students. To protect and prevent students, faculty, and staff members from the disease, higher education institutions closed their campus in the spring of 2020 and made a quick transition to online classes. Students were asked to evacuate on a short notice, adjust to new online learning environments, and lose their paid jobs in the middle of the semester. The pandemic has also raised concerns among college students about the health of their family and friends (Brown & Kafka, 2020 ). Because all these changes were unprecedented and intensive, they caused psychological distress among students, especially during the first few months of the pandemic. There is abundant anecdotal evidence describing students’ stress and emotional difficulties as impacted by COVID-19, but there are only a few empirical studies available that directly measure college student mental health since the outbreak (e.g., Huckins et al., 2020 ; Kecojevic et al., 2020 ; Son et al., 2020 ). Most existing studies focus on mental health for general populations (e.g., Gao et al., 2020 ) or health care workers (e.g., Chen et al., 2020 ), whose results may not be applicable to college students. Given that college students are particularly vulnerable to mental health issues (e.g., Kitzrow, 2003 ), it is important to explore their mental health during this unprecedented crisis.

In this study, we describe the prevalence of stress, anxiety, and depression for undergraduate students in a public research university during the six weeks after the COVID-19 outbreak alongside their usage of mental health services. Using a self-administered online survey, we measured stress, anxiety, and depression levels with well-established clinical tools and asked the extent to which college students used on-campus and off-campus mental health services for the academic year. Our results revealed that more than eight out of ten students surveyed experienced modest or severe stress, and approximately 36–44% of respondents showed moderate or severe anxiety and depression. However, more than 60% of students with moderate or severe stress, anxiety, or depression had never utilized mental health services on- or off-campus. Although focusing on a single institution, this paper is one of the few studies that empirically examine mental health of college students in the U.S. during the early phase of the pandemic. Findings from this paper reassure the seriousness of student mental health during the pandemic and call for a proactive mental health assessment and increased support for college students.

Literature Review

Covid-19 and student mental health.

Empirical studies reported a high prevalence of college mental health issues during the early phase of COVID-19 around the world (Cao et al., 2020 ; Chang et al., 2020 ; Liu et al., 2020 , Rajkumar, 2020 ; Saddik et al., 2020 ). In the U.S. a few, but a growing number of empirical surveys and studies were conducted to assess college students’ mental health during the pandemic. Three nationwide surveys conducted across the U.S. conclude that college student mental health became worse during the pandemic. According to an online survey administered by Active Minds in mid-April of 2020, 80% of college students across the country reported that COVID-19 negatively affected their mental health, with 20% reporting that their mental health had significantly worsened (Horn, 2020 ). It is also concerning that 56% of students did not know where to go if they had immediate needs for professional mental health services (Horn, 2020 ). Another nationwide survey conducted from late-May to early-June also revealed that 85% of college students felt increased anxiety and stress during the pandemic, but only 21% of respondents sought a licensed counselor or a professional (Timely MD, n.d. ) According to the Healthy Minds Network’s survey (2020), which collected data from 14 college campuses across the country between March and May of 2020, the percentage of students with depression increased by 5.2% compared to the year before. However, 58.2% of respondents never tried mental health care and about 60% of students felt that it became more difficult to access to mental health care since the pandemic. These survey results clearly illustrate that an overwhelming majority of college students in the U.S. have experienced mental health problems during the early phase of COVID-19, but far fewer students utilized professional help. Despite the timely and valuable information, only Healthy Minds Network ( 2020 ) used clinical tools to measure student mental health, and none of them explored whether student characteristics were associated with mental health symptoms.

To date, only a few scholarly research studies focus on college student mental health in the U.S. since the COVID-19 outbreak. Huckins et al. ( 2020 ) have longitudinally tracked 178 undergraduate students at Dartmouth University for the 2020 winter term (from early-January to late-March of 2020) and found elevated anxiety and depression scores during mid-March when students were asked to leave the campus due to the pandemic. The evacuation decision coincided with the final week, which could have intensified student anxiety and depression. The anxiety and depression scores gradually decreased once the academic term was over, but they were still significantly higher than those measured during academic breaks in previous years. Conducting semi-structured interviews with 195 students at a large public university in Texas, Son et al. ( 2020 ) found that 71% of students surveyed reported increased stress and anxiety due to the pandemic, but only 5% of them used counseling services. The rest of the students explained that they did not use counseling services because they assumed that others would have similar levels of stress and anxiety, they did not feel comfortable talking with unfamiliar people or over the phone, or they did not trust counseling services in general. Common stressors included concerns about their own health or their loved ones’, sleep disruption, reduced social interactions, and difficulty in concentration. Based on a survey from 162 undergraduate students in New Jersey, Kecojevic et al. ( 2020 ) found that female students had a significantly higher level of stress than male students and that upper-class undergraduate students showed a higher level of anxiety than first-year students. Having difficulties in focusing on academic work led to increased levels of stress, anxiety, and depression (Kecojevic et al., 2020 ).

College Student Mental Health and Usage of Mental Health Services Before COVID-19

College student mental health has long been studied in education, psychology, and medicine even before the pandemic. The general consensus of the literature is that college student mental health is in crisis, worsening in number and severity over time. Before the pandemic in the academic year of 2020, more than one-third of college students across the country were diagnosed by mental health professionals for having at least one mental health symptom (American College Health Association, 2020 ). Anxiety (27.7%) and depression (22.5%) were most frequently diagnosed. The proportion of students with mental health problems is on the rise as well. Between 2009 and 2015, the proportion of students with anxiety or depression increased by 5.9% and 3.2%, respectively (Oswalt et al., 2020 ). Similarly, between 2012 and 2020, scores for depression, general anxiety, and social anxiety have constantly increased among those who visited counseling centers on college campuses (Center for College Mental Health [CCMH], 2021 ).

Some groups are more vulnerable to mental health problems than others. For example, female and LGBTQ students tend to report a higher prevalence of mental health issues than male students (Eisenberg et al., 2007b ; Evans et al., 2018 ; Wyatt et al., 2017 ). However, there is less conclusive evidence on the difference across race or ethnicity. It is well-supported that Asian students and international students report fewer mental health problems than White students and domestic students, but there are mixed results regarding the difference between underrepresented racial minority students (i.e., African-American, Hispanic, and other races) and White students (Hyun et al., 2006 ; Hyun et al., 2007 ). Many researchers find either insignificant differences (e.g., Eisenberg et al., 2007b ) or fewer mental health issues reported for underrepresented minority students compared to White students (e.g., Wyatt et al., 2017 ). This may not necessarily mean that racial minority students tend to have fewer mental health problems, but it may reflect their cultural tendency against disclosing one’s mental health issues to others (Hyun et al., 2007 ; Wyatt & Oswalt, 2013 ). In terms of age, some studies (e.g., Eisenberg et al., 2007b ) reveal that students who are 25 years or older tend to have fewer mental health issues than younger students, while others find it getting worse throughout college (Wyatt et al., 2017 ). Lastly, financial stress significantly increases depression, anxiety, and suicidal thoughts among college students (Eisenberg et al., 2007b ).

Despite the high prevalence of mental health issues, college students tend to underutilize mental health services (Cage et al., 2018 ; Hunt & Eisenberg, 2010 ; Lipson et al., 2019 ; Oswalt et al., 2020 ). The Healthy Minds Study 2018–2019, which collected data from 62,171 college students across the country, reports that 57% of students with positive anxiety or depression screens have not used counseling or therapy, and 64% of them have not taken any psychotropic medications within the past 12 months (Healthy Minds, 2019 ). Even when students had visited a counseling center, about one-fourth of them did not return for a scheduled appointment, and another 14.1% of students declined further services (CCMH, 2021 ). When asked the barriers that prevented them from seeking mental health services, students reported a lack of perceived needs for help (41%), preference to deal with mental health issues on their own or with families and friends (27%), a lack of time (23%), financial difficulty (15%), and a lack of information about where to go (10%). Students who never used mental health services were not sure if their insurance covered mental health treatment or were more skeptical about the effectiveness of treatment (Eisenberg et al., 2007a ). Stigma, students’ view about getting psychological help for themselves, is another significant barrier in seeking help and utilizing mental health services (Cage et al., 2018 ).

Current Study

While previous studies have advanced our understanding of student mental health and their usage of mental health services, we find a lack of empirical studies on these matters, particularly in the context of COVID-19. The goal of this study is to fill the gap with specific investigations into the prevalence and pattern of U.S. college student mental health with regard to counseling service use during the early phase of COVID-19. First, very few studies focus on college students and their mental health during the pandemic, and most nationwide surveys conducted in the U.S. did not use clinically validated tools to measure student mental health. In this study, we have employed the three clinical measures to assess stress, anxiety, and depression, which are the most prevalent mental health problems among college student populations (Leviness et al., 2017 ). Secondly, it should be noted that while empirical research conducted in U.S. institutions clearly demonstrate that college students were under serious mental distress during the pandemic (Huckins et al., 2020 ; Son et al., 2020 ; Kecojevic et al., 2020 ), such studies have relatively small sample sizes and rarely examined whether particular groups were more vulnerable than others during the pandemic. To overcome such limitations, the present study has recruited a relatively large number of students from all degree-seeking students enrolled at the study institution. Further, given the high prevalence of mental health issues, we have identified vulnerable student groups and provided suggestions regarding necessary support for these students in an effort to reduce mental health disparity. Lastly, previous studies (e.g., Healthy Minds, 2019 ) show that college students, even those with mental health issues, tended to underutilize counseling services before the pandemic. Yet, there is limited evidence regarding whether this continued to be the case during COVID-19. Our study provides empirical evidence regarding the utilization of mental health services during the early phase of the pandemic and identifies its predictors. Based on the preceding discussions, we address the following research questions in this study:

First, how prevalent were stress, anxiety, and depression among college students during the early phase of the pandemic? Second, to what extent have students utilized mental health services on- and off-campus? Third, what are the predictors of mental health symptoms and the usage of mental health services?

We collected data via a self-administered online survey. This survey was designed to measure student mental health, the usage of mental health services, and demographics. The survey was sent to all degree-seeking students enrolled in a public research university in Kentucky for the spring of 2020. An invitation email was first sent on March 23, which was two days after the university announced campus closure, and two more reminder emails were sent in mid-April and late-April. The survey was available until May 8th, which was the last day of the semester.

A total of 2691 students (out of 24,146 qualified undergraduate and graduate degree-seeking students enrolled for the semester) responded to the survey. The response rate was 11.14%, but this is acceptable as it is within the range of Internet survey response rates, which is anywhere from 1 to 30% (Wimmer & Dominick, 2006 ). We deleted responses from 632 students who did not answer any mental health questions, which left 2059 valid students for the analysis. In this study, we focused on undergraduate students because they are significantly different from graduate students in terms of demographics (e.g., racial composition, age, and income) and major stressors (Wyatt & Oswalt, 2013 ). As a result, 1412 undergraduate students are included in our sample. 90% of these students had complete data. The rest of students skipped a couple of questions (usually related to their residency) but answered most of the question. Thus, we conducted multiple imputation, created ten imputed data sets, and ran regression models using these imputed data (Allison, 2002 ). Our regression results using imputed data are qualitatively similar to the estimates using original data; however, for comparison, we also provided the regression estimates using original data in Appendix Tables  6 and ​ and7. 7 . Please note that we still used original data for descriptive research questions (presented in Tables  1 , ​ ,2, 2 , and ​ and4) 4 ) to accurately describe the prevalence of mental health symptoms and use of counseling services.

Descriptive statistics of sample characteristics

Descriptive statistics for stress, anxiety, and depression prevalence

Usage of mental health services among students with moderate or severe symptoms

Ordinal logistic regression models for severity of mental health symptoms (original data)

Odds ratio are reported, and numbers in parentheses are standard error

+ p  < 0.1, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Logistic regression models predicting the usage of mental health services (original data)

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table  1 provides descriptive statistics for students in our data. Female (73%), White (86%), and students who are below 25 years old (95%) are the vast majority of our sample. About one in four students are rural students and/or students from Appalachian areas (27%) and first-generation students (23%). Wealthier students (whose family income was $100,000 or more) make up about 44% of the sample (44%). Compared to the undergraduate student population at the study site, female students (56.3% at the study site) are overrepresented in our study. The proportion of White students is slightly higher in our sample (86%) than the study population (84%), and that of first-generation students is slightly lower in our sample (23%) than that in the study population (26%).

There are five key outcome variables for this study. The first three outcome variables are stress, anxiety, and depression, and the other two variables are the extent to which students used on-campus and off-campus mental health services for the academic year, respectively. Our mental health measures are well-established and widely used in a clinical setting. For stress, we used the Perceived Stress Scale (PSS) that includes ten items asking students’ feelings and perceived stress measured on a 5-point Likert scale from 0 (strongly disagree) to 4 (strongly agree) (Cohen et al., 1983 ). Using the sum of scores from the ten items, the cut-off score for low, moderate, and high stress is 13, 26, and 40, respectively. PSS scale was used in hundreds of studies and validated in many languages (Samaha & Hawi, 2016 ). PSS also has a high internal consistency reliability. Of the recent studies that used the instrument to measure mental health of U.S. college students, Cronbach’s alpha was around 0.83 to 0.87, which exceeded the commonly used cut-off of 0.70 (Adams et al., 2016 ; Burke et al., 2016 ; Samaha & Hawi, 2016 ).

We used the General Anxiety Disorder 7-item (GAD-7) scale to measure anxiety. This is a brief self-report scale to identify probable cases of anxiety disorders (Spitzer et al., 2006 ). The GAD scores of 5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, respectively. In a clinical setting, anyone with a score of 10 or above are recommended for further evaluation. GAD is moderately good at screening three other common anxiety disorders - panic disorder (sensitivity 74%, specificity 81%), social anxiety disorder (sensitivity 72%, specificity 80%), and post-traumatic stress disorder (sensitivity 66%, specificity 81%) (Spitzer et al., 2006 ) In their recent study, Johnson, et al. ( 2019 ) validated that “the GAD-7 has excellent internal consistency, and the one-factor structure in a heterogeneous clinical population was supported” (p. 1).

Lastly, depression was assessed with the eight-item Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Short Form (Pilkonis et al., 2014 ). A score less than 17 is considered as none to slight depression, a score between 17 and 21 is considered as mild depression, a score between 22 and 32 is considered as moderate depression, and a score of 33 or above is considered as severe depression. PROMIS depression scale is a universal, rather than a disease-specific, measure that was developed using item response theory to promote greater precision and reduce respondent burden (Shensa et al., 2018 ). The scale has been correlated and validated with other commonly used depression instruments, including the Center for Epidemiological Studies Depression Scale (CES-D), the Beck Depression Inventory (BDI-II), and the Patient Health Questionnaire (PHQ-9) (Lin et al., 2016 ).

When it comes to the usage of psychological and counseling services, we asked students to indicate the extent to which they used free on-campus resources (e.g., counseling center) and off-campus paid health professional services (e.g., psychiatrists) anytime during the academic year on a scale of 1 (never) to 5 (very often), respectively. These questions do not specifically ask if students utilized these services after the COVID-19 outbreak, but responses for these questions indicate whether and how often students had used any of these services for the academic year until they responded to our survey.

We also collected data about student demographics and characteristics including student gender, race or ethnicity, age, class levels (freshman, sophomore, junior, and senior), first generation student status (1 = neither parent has a bachelor’s degree, 0 = at least one parent with a bachelor’s degree), family income, residency (rural and/or Appalachian students, international students), GPAs, and perceived stigma about seeking counseling or therapy (i.e., “I am afraid of what my family and friends will say or think of me if I seek counseling/therapy”) measured on a 5-point Likert scale. We used these variables to see if they were associated with a high level of stress, anxiety, and depression and the usage of mental health services.

We used descriptive statistics, ordinal logistic regression, and logistic regression models in this study. To address the first and second research questions, we used descriptive statistics and presented the prevalence of stress, anxiety, and depression as well as the frequency of using mental health services. For the third research question, we adopted ordinal logistic regression and logistic regression models depending on outcome variables. We used ordinal logistic regression models to identify correlates of different levels of stress, anxiety, and depression, which were measured in ordinal variables (e.g., mild, moderate, and severe). For the usage of mental health service outcomes, we employed logistic regression models. Because more than two-thirds of students in the sample never utilized either type of mental health services, we re-coded the usage variables into binary variables (1 = used services, 0 = never used services) and ran logistic regression models.

Limitations

Our study is not without limitations. First, we do not claim a causal relationship in this study, but we describe the state of mental health for students soon after the COVID-19 outbreak. We acknowledge that many students may have suffered from mental health problems before the pandemic, with some experiencing escalation after the outbreak (e.g., Horn, 2020 ). Even if our study does not provide a causal relationship, we believe that it is important to measure and document student mental health during the pandemic so that practitioners can be aware of the seriousness of this issue and consider ways to better serve students. Secondly, our study results may not be applicable to students in other institutions or states. We collected data from a public research university in Kentucky where the number of confirmed cases and deaths were relatively lower than other states such as New York. The study site mainly serves traditional college students who attend college right after high school, who live on campus, and who do not have dependents. Therefore, mental health for students at other types of institutions or in other states could be different from what is presented in our study.

Prevalence of Stress, Anxiety, and Depression

Table  2 shows the prevalence of stress, anxiety, and depression. Overall, a majority of students experienced psychological distress during the early phase of the pandemic. When it comes to stress, about 63% of students had a moderate level of stress, and another 24.61% of students fell into a severe stress category. Only 12% of students had a low level of stress. In other words, more than eight in ten students in the survey experienced moderate to severe stress during the pandemic. This result is comparable to the Active Minds’ survey results that report 91% of college students reported experiencing feelings of stress and anxiety since the pandemic (Horn, 2020 ).

In terms of anxiety, approximately 24% and 21% of students in our study had moderate and severe anxiety disorders, respectively. Given that those who scored 10 or above on the GAD-7 scale (moderate to severe category) are recommended to meet with professionals (Spitzer et al., 2006 ), this finding implies that nearly half of students in this study needed to get professional help. This proportion of students with moderate to severe anxiety is almost double that for university students in China (e.g., Chang et al., 2020 ) or the United Arab Emirates soon after the COVID-19 outbreak (Saddik et al., 2020 ). Lastly, approximately 30% and 6% of students suffered from moderate and severe depression, respectively. These proportions are far higher than college students in China measured during the pandemic (Chang et al., 2020 ) but slightly higher than a nationwide sample of U.S. college students assessed before the pandemic (Healthy Minds, 2019 ). Given that our study measured these mental health symptoms for the first six weeks of the pandemic, we speculate that the proportion of students with moderate or severe depression would increase over time.

In order to explore predictors of a higher level of stress, anxiety, and depression, we ran ordinal logistic regression models as presented in Table  3 . Overall, it is clear and consistent that the odds of experiencing a higher level of stress, anxiety, and depression (e.g., severe than moderate, moderate than mild, etc.) were significantly greater for female students by a factor of 1.489, 1.723, and 1.246 than the odds for male students when other things were held constant. This gender difference in mental health symptoms is quite consistent with other studies before and during the pandemic (Eisenberg et al., 2007a ; Kecojevic et al., 2020 ). When it comes to race or ethnicity, the odds of experiencing a higher level of stress, anxiety, and depression for African-American students were almost as half as the odds for White students. However, there was no significant difference in the odds for Hispanic and Asian students compared to White students. Student class level was significantly related to stress and anxiety levels: The odds were greater for upper-class students than lower class students. This result is consistent with Kecojevic et al. ( 2020 ), which reported significantly higher levels of anxiety among upper-class students compared to freshman students. It may reflect that one of major stressors for college students during the pandemic is the uncertain future of their education and job prospects, which would be a bigger concern for upper-class students (Timely MD, n.d.).

Ordinal logistic regression models for severity of mental health symptoms (imputed data)

One’s rurality, family income, and GPA were significantly associated with the severity of mental health symptoms. The odds of experiencing a severe level of anxiety and depression were 1.325 and 1.270 times higher among rural students than urban and suburban students. With every one unit increase in family income or students’ GPAs, the odds of experiencing a more severe stress, anxiety, and depression significantly decreased. This result suggests that students from disadvantaged backgrounds were even more vulnerable to psychological distress during the early phase of the pandemic. The negative association between GPAs and mental distress levels was consistent with previous studies that showed that college students were very concerned about their academic performances and had difficulty in concentration during the early phase of the pandemic (Kecojevic et al., 2020 ; Son et al., 2020 ).

Usage of Mental Health Services

In Table  4 , we first describe the extent to which students with moderate to severe symptoms of stress, anxiety, or depression used mental health services on- and off-campus during the academic year. The university in this study has provided free counseling services for students, and the counseling services have continued to be available for students in the state via phone or Internet even after the university was closed after the outbreak. Table ​ Table4 4 presents the frequency of students using on-campus mental health services (Panel A) and off-campus paid mental health services (Panel B) on a five-point scale. For this table, we limited the sample to students with moderate to severe symptoms of stress, anxiety, or depression to focus on students who were in need of these services. Surprisingly, a majority of these students never used mental health services on- and off-campus even when their stress, anxiety, or depression scores indicated that they needed professional help. More than 60% of students with moderate to severe symptoms never used on-campus services, and more than two-thirds of students never used off-campus mental health services. This underutilization of mental health resources is concerning but not surprising given that college students tended not to use counseling services before and during the pandemic as presented in previous studies (e.g., CCMH, 2021 ; Healthy minds, 2019 ; Son et al., 2020 ).

In order to explore predictors of the usage of mental health services, we ran logistic regression models as shown in Table  5 . We included all students in these regression models to see whether a severity of mental health symptoms was related to the usage of mental health services. Table ​ Table5 5 presents the results for the usage of any mental health services, on-campus mental health services, and off-campus mental health services, respectively. Overall, stress, anxiety, and depression levels were positively associated with using mental health services on- and off-campus: With every one unit increase in each of these mental health symptoms, the odds of using on- and off-campus mental health services significantly increased. This result is relieving as it suggests that students who were in great need of these services actually used them. Other than mental health symptoms, there were different predictors for utilizing on-campus and off-campus services. African-American and Hispanic students were significantly more likely to use on-campus services than White students. The odds of using on-campus mental health services were 3.916 times higher for African-American students and 2.032 times higher for Hispanic students than White students. This result is interesting given that the odds of having severe mental distress were significantly lower for African-American students than White students, according to Table ​ Table3. 3 . It may suggest that African-American students reported relatively lower levels of mental health symptoms as they had been using on-campus mental health services at higher rates. The odds of using on-campus mental health services were 2.269 times higher for international students than domestic students, but there was no significant difference in the odds of using off-campus services between the two groups. Students’ age was significantly associated with the usage of on-campus and off-campus mental health services: The odds of using on-campus services were significantly lower for older students, while the odds of utilizing off-campus services were significantly higher for older students compared to younger students. When it comes to using off-campus mental health services, the odds were significantly higher for female students, older students, and upper-class students than male students, younger students, and lower classman students. Students who were concerned with stigma associated with getting counseling and therapy were less likely to utilize off-campus mental health services.

Logistic regression models predicting the usage of mental health services (imputed data)

Discussions

Our paper describes the prevalence of stress, anxiety, and depression among a sample of undergraduate students in a public research university during an early phase of the COVID-19 outbreak. Using well-established clinical tools, we find that stress, anxiety, and depression were the pervasive problems for college student population during the pandemic. In particular, female, rural, low-income, and academically low-performing students were more vulnerable to psychological distress. Despite its prevalence, about two-thirds of students with moderate to severe symptoms had not utilized mental health services on- and off-campus. These key findings are very concerning considering that mental health is strongly associated with student well-being, academic outcomes, and retention (Bruffaerts et al., 2018 ; Wyatt et al., 2017 ).

Above all, we reiterate that college student mental health is in crisis during the pandemic and call for increased attention and interventions on this issue. More than eight in ten students in our study had moderate to severe stress, and more than one thirds of students experienced moderate to severe anxiety and/or depression. This is much worse than American college students before the COVID-19 (e.g., American College Health Association, 2020 ) and postsecondary students in other countries during the pandemic (e.g., Chang et al., 2020 ; Saddik et al., 2020 ). In particular, rural students, low-income students, and students with low GPAs were more vulnerable to psychological distress. These students have already faced multiple barriers in pursuing higher education (e.g., Adelman, 2006 ; Byun et al., 2012 ), and additional mental health issues would put them at a high risk of dropping out of college. Lastly, although they were dropped from the main analysis due to the small sample size ( n  = 17), it is still noteworthy that a significantly higher proportion of LGBTQ students in our sample experienced severe stress, anxiety, and depression, which calls for significant attention and care for these students.

Despite the high prevalence of mental health problems, a majority of students with moderate to severe symptoms never used mental health services during the academic year, even though the university provided free counseling services. This result could be partially explained by the fact that the university’s counseling center switched to virtual counseling since the COVID-19 outbreak, which was available only for students who stayed within the state due to the license restriction across state boarders. This transition could limit access to necessary care for out-of-state students, international students, or students in remote areas where telecommunications or the internet connection is not very stable. Even worse, these students may also have limited access to off-campus health professionals due to the geographic restrictions (rural students), limited insurance coverage (international students), or a lack of financial means. Our results support that international students relied significantly more on on-campus resources than domestic students. We urge practitioners and policy makers to provide additional mental health resources that are accessible, affordable, and available for students regardless of their locations, insurance, and financial means, such as informal peer conversation groups or regular check-ins via phone calls or texts.

It is also important to point out that the overall usage of both on-campus and off-campus mental health services was generally low even before the COVID-19 outbreak. Previous studies consistently report that college students underutilize mental health services not only because of a lack of information, financial means, or available seats but also because of a paucity of perceived needs or stigma related to revealing one’s mental health issues to others (Cage et al., 2018 ; Eisenberg et al., 2007a ; Son et al., 2020 ). Our results support this finding by demonstrating that stigma one associated with getting counseling or therapy negatively influenced their utilization of off-campus mental health services. Considering these barriers, practitioners should deliver a clear message publicly that mental health problems are very common among college students and that it is natural and desirable to seek professional help if students feel stressed out, anxious, or depressed. In order to identify students with mental health needs and raise awareness among students, it can be also considered to administer a short and validated assessment in classes that enroll a large number of students (e.g., in a freshman seminar course), inform the entire class of how to interpret their scores on their own, and provide a list of available resources for those who may be interested. This would give students a chance to self-check their mental health without revealing their identities and seek help, if necessary.

We recommend that future researchers longitudinally track students and see whether the prevalence of mental health problems changes over time. Longitudinal studies are generally scarce in student mental health literature, but the timing of assessment can influence mental health symptoms reported (Huckins et al., 2020 ). The survey for our study was sent out right after the university of this study was closed due to the pandemic. It is possible that students may adjust to the outbreak over time and feel better, or that their stress may add up as the disease progresses. Tracking students over time can illustrate whether and how their mental health changes, especially depending on the way the pandemic unfolds combined with the cycle of an academic year. Secondly, there should be more studies that evaluate the effect of an intervention program on student mental health. Hunt and Eisenberg ( 2010 ) point out that little has been known about the efficacy of intervention programs while almost every higher education institution offers multiple mental health resources and counseling programs. During this pandemic, it can be a unique opportunity to implement virtual mental health interventions and evaluate their efficacy. Future research on virtual counseling and mental health interventions would guide practices to accommodate mental health needs for students who exclusively take online courses or part-time students who spend most of their time off campus. Lastly, we recommend future research investigate the extent of mental health service utilization among students with mental health needs. Existing surveys and studies on this topic usually rely on responses from those who visit a counseling center or students who respond to their surveys. Neither of these groups accurately represents those who are in need of professional help because there may be a number of students who are not aware of their mental health issues or do not want to reveal it. An effective treatment should first start with identifying those in need.

Our study highlights that college students are stressed, anxious, and depressed in the wake of COVID-19. Although college students have constantly reported mental health issues (e.g., American College Health Association, 2020 ), it is remarkable to note that the broad spectrum of COVID-19-related challenges may mitigate the overall quality of their psychological wellbeing. This is particularly the case for at-risk students (rural, international, low-income, and low-achieving students) who have already faced multiple challenges. We also present that a majority of students with mental health needs have never utilized on- and off-campus services possibly due to the limited access or potential stigma associated with mental health care. Systematic efforts with policy makers and practitioners are requested in this research to overcome the potential barriers. All these findings, based on the clinical assessment of student mental health during the early phase of the pandemic, will benefit scholars and practitioners alike. As many colleges and universities across the country have re-opened their campus for the 2020–2021 academic year, students, especially those who take in-person classes, would be concerned about the disease and continuing their study in this unprecedented time. On top of protecting students from the disease by promoting wearing masks and social distancing, it is imperative to pay attention to their mental health and make sure that they feel safe and healthy. To this end, higher education institutions should proactively reach out to all student populations, identify students at risk of mental health issues, and provide accessible and affordable care.

Biographies

is Assistant Professor of Higher Education at the University of Kentucky. She studies higher education policy, program, and practice and their effects on student success.

is an Assistant Professor of Integrated Strategic Communication at the University of Kentucky. She earned her Ph.D. in Media and Information Studies at Michigan State University. Her research interests include prosocial campaigns, consumer wellbeing, and civic engagement.

is an associate professor in the Division of Biomedical Informatics in the College of Medicine at the University of Kentucky. Dr. Kim’s current research includes: consumer health informatics, personal health information management, and health information seeking behaviors. She uses clinical natural language professing techniques and survey methodologies to better understand patients’ health knowledge and their health information uses and behaviors.

Author’s Contribution

The order of the authors in the title page reflects the share of each author’s contribution to the manuscript.

Data Availability

Code availability, declarations.

The authors declare that they have no conflicts of interest.

All authors agree to publish this paper.

Publisher’s Note

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Contributor Information

Jungmin Lee, Email: [email protected] .

Hyun Ju Jeong, Email: [email protected] .

Sujin Kim, Email: ude.yku@miknijus .

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Mental Health Research from Oxford Journals

Select medicine and health journals from OUP have collaborated to curate a collection of notable articles on the focus of mental health. The research covers a wide-range of areas including music therapy, sleep, age and the brain. Please enjoy the selection, available freely through December 31, 2018.

Schizophrenia Bulletin: The Journal of Psychoses and Related Disorders

Too Fast or Too Slow? Time and Neuronal Variability in Bipolar Disorder—A Combined Theoretical and Empirical Investigation

Social Identity and Psychosis: Associations and Psychological Mechanisms

Depression and Schizophrenia: Cause, Consequence, or Trans-diagnostic

Trauma-Focused Treatment in PTSD Patients With Psychosis: Symptom Exacerbation, Adverse Events, and Revictimization

Journal of Pediatric Psychology

Child Executive Control as a Moderator of the Longitudinal Association Between Sleep Problems and Subsequent Attention-Deficit/Hyperactivity Disorder Symptoms

Topical Review: Transitional Services for Teens and Young Adults With Attention-Deficit Hyperactivity Disorder: A Process Map and Proposed Model to Overcoming Barriers to Care

Hope as a Predictor of Anxiety and Depressive Symptoms Following Pediatric Cancer Diagnosis

Commentary: Adolescent Marijuana Use and Mental Health Amidst a Changing Legal Climate

Associations of Child Insomnia, Sleep Movement, and Their Persistence With Mental Health Symptoms in Childhood and Adolescence

Persistent Insomnia: the Role of Objective Short Sleep Duration and Mental Health

Inflammatory Bowel Diseases

The Validity and Reliability of Screening Measures for Depression and Anxiety Disorders in Inflammatory Bowel Disease

Controversies surrounding the comorbidity of depression and anxiety in inflammatory bowel disease patients: A literature review

Archives of Clinical Neuropsychology

Profile Analyses of the Personality Assessment Inventory Following Military-Related Traumatic Brain Injury

International Journal of Neuropsychopharmacology

Anti-dementia Drugs for Psychopathology and Cognitive Impairment in Schizophrenia: A Systematic Review and Meta-analysis

MDD Virtual Issue

Music Therapy Perspectives

Music Therapy Practices and Processes with Foster-Care Youth: Formulating an Approach to Clinical Work

On the Neural Mechanisms of Music Therapy in Mental Health Care: Literature Review and Clinical Implications

Journal of Music Therapy

Experiences of Persons With Parkinson’s Disease Engaged in Group Therapeutic Singing

Annals of Work Exposures and Health

Job Stressors and Employment Precarity as Risks for Thoughts About Suicide: An Australian Study Using the Ten to Men Cohort

Examining Exposure Assessment in Shift Work Research: A Study on Depression Among Nurses

Working hours and common mental disorders in English police officers

Impact of working hours on sleep and mental health

Social Cognitive and Affective Neuroscience

Amygdala–medial prefrontal cortex connectivity relates to stress and mental health in early childhood

Like mother like daughter: putamen activation as a mechanism underlying intergenerational risk for depression

International Journal of Epidemiology

Do material, psychosocial and behavioural factors mediate the relationship between disability acquisition and mental health? A sequential causal mediation analysis

The intergenerational consequences of war: anxiety, depression, suicidality, and mental health among the children of war veterans

Neuro-oncology Practice

Health-related quality of life and psychological functioning in patients with primary malignant brain tumors: a systematic review of clinical, demographic and mental health factors

A Refuge for Some: Gender Differences in the Relationship between Religious Involvement and Depression

Prayer, Attachment to God, and Symptoms of Anxiety - Related Disorders among U.S. Adults

Journal of Public Health

Trends in mental health outcomes and combat exposure among US marines returning from Iraq, Afghanistan or other deployments, 2004–13

A multisite, longitudinal study of risk factors for incarceration and impact on mental health and substance use among young transgender women in the USA

Age and Ageing

The long-arm of adolescent weight status on later life depressive symptoms

A preventative lifestyle intervention for older adults (lifestyle matters): a randomised controlled trial

International Journal of Quality in Health Care

A national evaluation of community-based mental health strategies in Finland

Routine quality care assessment of schizophrenic disorders using information systems

European Journal of Public Health

Are mental health systems responsive to the mental health needs of Syrian refugees?

Understanding race, mental health, and wellbeing: a progressive study of experienced racial othering in the UK and the US

Below the surface: a systematic review on the mental health problems, sources of stress and coping among female foreign domestic workers

Alcohol and Alcoholism

The Effect of Brief Interventions for Alcohol Among People with Comorbid Mental Health Conditions: A Systematic Review of Randomized Trials and Narrative Synthesis

Oxytocin Genotype Moderates the Impact of Social Support on Psychiatric Distress in Alcohol-Dependent Patients

Health Policy and Planning

Psychosocial support for adolescent girls in post-conflict settings: beyond a health systems approach

Interventions and approaches to integrating HIV and mental health services: a systematic review

British Journal of Social Work

Developing a Health Inequalities Approach for Mental Health Social Work

The Mental Health and Help-Seeking Behaviour of Children and Young People in Care in Northern Ireland: Making Services Accessible and Engaging

Journal of Gerontology Series B

Association of Anxiety Symptom Clusters with Sleep Quality and Daytime Sleepiness

Depressive Symptoms as a Predictor of Memory Complaints in the PRISM Sample

Journal of Refugee Studies

Coming Out Under the Gun: Exploring the Psychological Dimensions of Seeking Refugee Status for LGBT Claimants in Canada

Health Promotion International

Promoting mental wellbeing among older people: technology-based interventions

Mental health message appeals and audience engagement: Evidence from Australia

Nicotine and Tobacco Research

An Adaptation of Motivational Interviewing Increases Quit Attempts in Smokers With Serious Mental Illness

Diverging Trends in Smoking Behaviors According to Mental Health Status

Journal of Clinical Endocrinology and Metabolism

Polycystic ovary syndrome is associated with adverse mental health and neurodevelopmental outcomes

Endocrinology

Desacyl Ghrelin Decreases Anxiety-like Behavior in Male Mice

Journal of the Endocrine Society

Altered Pituitary Gland Structure and Function in Posttraumatic Stress Disorder

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  • Review Article
  • Open access
  • Published: 08 April 2022

Natural language processing applied to mental illness detection: a narrative review

  • Tianlin Zhang   ORCID: orcid.org/0000-0003-0843-1916 1 ,
  • Annika M. Schoene 1 ,
  • Shaoxiong Ji   ORCID: orcid.org/0000-0003-3281-8002 2 &
  • Sophia Ananiadou 1 , 3  

npj Digital Medicine volume  5 , Article number:  46 ( 2022 ) Cite this article

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  • Disease prevention
  • Psychiatric disorders

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

Introduction

Mental illnesses, also called mental health disorders, are highly prevalent worldwide, and have been one of the most serious public health concerns 1 . There are many different mental illnesses, including depression, suicidal ideation, bipolar disorder, autism spectrum disorder (ASD), anxiety disorder, schizophrenia, etc., any of which can have a negative influence on an individual’s physical health and well-being with the problem exacerbated due to Covid-19 2 . According to the latest statistics, millions of people worldwide suffer from one or more mental disorders 1 . If mental illness is detected at an early stage, it can be beneficial to overall disease progression and treatment.

There are different text types, in which people express their mood, such as social media messages on social media platforms, transcripts of interviews and clinical notes including the description of patients’ mental states. In recent years, natural language processing (NLP), a branch of artificial intelligence (AI) technologies, has played an essential role in supporting the analysis and management of large scale textual data and facilitating various tasks such as information extraction, sentiment analysis 3 , emotion detection, and mental health surveillance 4 , 5 , 6 . Detecting mental illness from text can be cast as a text classification or sentiment analysis task, where we can leverage NLP techniques to automatically identify early indicators of mental illness to support early detection, prevention and treatment.

Existing reviews introduce mainly the computational methods for mental health illness detection, they mostly focus on specific mental illnesses (suicide 7 , 8 , 9 , depression 10 , 11 , 12 ), or specific data sources (social media 13 , 14 , 15 , non-clinical texts 16 ). To the best of our knowledge, there is no review of NLP techniques applied to mental illness detection from textual sources recently. We present a broader scope of mental illness detection using NLP that covers a decade of research, different types of mental illness and a variety of data sources. Our review aims to provide a comprehensive overview of the latest trends and recent NLP methodologies used for text-based mental illness detection, and also points at the future challenges and directions. Our review seeks to answer the following questions:

What are the main NLP trends and approaches for mental illness detection?

Which features have been used for mental health detection in traditional machine learning-based models?

Which neural architectures have been commonly used to detect mental illness?

What are the main challenges and future directions in NLP for mental illness?

Search methodology

Search strategy.

A comprehensive search was conducted in multiple scientific databases for articles written in English and published between January 2012 and December 2021. The databases include PubMed, Scopus, Web of Science, DBLP computer science bibliography, IEEE Xplore, and ACM Digital Library.

The search query we used was based on four sets of keywords shown in Table 1 . For mental illness, 15 terms were identified, related to general terms for mental health and disorders (e.g., mental disorder and mental health), and common specific mental illnesses (e.g., depression, suicide, anxiety). For data source, we searched for general terms about text types (e.g., social media, text, and notes) as well as for names of popular social media platforms, including Twitter and Reddit. The methods and detection sets refer to NLP methods used for mental illness identification.

The keywords of each sets were combined using Boolean operator “OR", and the four sets were combined using Boolean operator “AND". We conducted the searches in December 2021.

Filtering strategy

A total of 10,467 bibliographic records were retrieved from six databases, of which 7536 records were retained after removing duplication. Then, we used RobotAnalyst 17 , a tool that minimizes the human workload involved in the screening phase of reviews, by prioritizing the most relevant articles for mental illness based on relevancy feedback and active learning 18 , 19 .

Each of the 7536 records was screened based on title and abstract. Records were removed if the following exclusion criteria were met: (1) the full text was not available in English; (2) the abstract was not relevant to mental illness detection; (3) the method did not use textual experimental data, but speech or image data.

After the screening process, 611 records were retained for further review. An additional manual full-text review was conducted to retain only articles focusing on the description of NLP methods only. The final inclusion criteria were established as follow:

Articles must study textual data such as contents from social media, electronic health records or transcription of interviews.

They must focus on NLP methods for mental illness detection, including machine learning-based methods (in this paper, the machine learning methods refer to traditional feature engineering-based machine learning) and deep learning-based methods. We exclude review and data analysis papers.

They must provide a methodology contribution by (1) proposing a new feature extraction method, a neural architecture, or a novel NLP pipeline; or (2) applying the learning methods to a specific mental health detection domain or task.

Following the full-text screening process, 399 articles were selected. The flow diagram of the article selection process is shown in Fig. 1 .

figure 1

Six databases (PubMed, Scopus, Web of Science, DBLP computer science bibliography, IEEE Xplore, and ACM Digital Library) were searched. The flowchart lists reasons for excluding the study from the data extraction and quality assessment.

Data extraction

For each selected article, we extracted the following types of metadata and other information:

Year of publication.

The aim of research.

The dataset used, including type of mental illness (e.g., depression, suicide, and eating disorder), language, and data sources (e.g., Twitter, electronic health records (EHRs) and interviews).

The NLP method (e.g., machine learning and deep learning) and types of features used (e.g., semantic, syntactic, and topic).

We show in Fig. 2 the number of publications retrieved and the methods used in our review, reflecting the trends of the past 10 years. We can observe that: (1) there is an upward trend in NLP-driven mental illness detection research, suggesting the great research value and prospects for automatic mental illness detection from text (2) deep learning-based methods have increased in popularity in the last couple of years.

figure 2

The trend of the number of articles containing machine learning-based and deep learning-based methods for detecting mental illness from 2012 to 2021.

In the following subsections, we provide an overview of the datasets and the methods used. In section Datesets, we introduce the different types of datasets, which include different mental illness applications, languages and sources. Section NLP methods used to extract data provides an overview of the approaches and summarizes the features for NLP development.

In order to better train mental illness detection models, reliable and accurate datasets are necessary. There are several sources from which we can collect text data related to mental health, including social media posts, screening surveys, narrative writing, interviews and EHRs. At the same time, for different detection tasks, the datasets also differ in the types of illness they focus on and language. We show a comprehensive mapping of each method with its associated application using a Sankey diagram (Fig. 3 ).

figure 3

The different methods with their associated application are represented via flows. Nodes are represented as rectangles, and the height represents their value. The width of each curved line is proportional to their values.

Data sources

Figure 4 illustrates the distribution of the different data sources. It can be seen that, among the 399 reviewed papers, social media posts (81%) constitute the majority of sources, followed by interviews (7%), EHRs (6%), screening surveys (4%), and narrative writing (2%).

figure 4

The pie chart depicts the percentages of different textual data sources based on their numbers.

Social media posts

The use of social media has become increasingly popular for people to express their emotions and thoughts 20 . In addition, people with mental illness often share their mental states or discuss mental health issues with others through these platforms by posting text messages, photos, videos and other links. Prominent social media platforms are Twitter, Reddit, Tumblr, Chinese microblogs, and other online forums. We briefly introduce some popular social media platforms.

Twitter. Twitter is a popular social networking service with over 300 million active users monthly, in which users can post their tweets (the posts on Twitter) or retweet others’ posts. Researchers can collect tweets using available Twitter application programming interfaces (API). For example, Sinha et al. created a manually annotated dataset to identify suicidal ideation in Twitter 21 . Hu et al. used a rule-based approach to label users’ depression status from the Twitter 22 . However, normally Twitter does not allow the texts of downloaded tweets to be publicly shared, only the tweet identifiers—some/many of which may then disappear over time, so many datasets of actual tweets are not made publicly available 23 .

Reddit . Reddit is also a popular social media platform for publishing posts and comments. The difference between Reddit and other data sources is that posts are grouped into different subreddits according to the topics (i.e., depression and suicide). Because of Reddit’s open policy, their datasets are publicly available. Yates et al. established a depression dataset named “Reddit Self-reported Depression Diagnosis" (RSDD) 24 , which contains about 9k depressed users and 100k control users. Similarly, CLEF risk 2019 shared task 25 also proposed an anorexia and self-harm detection task based on the Reddit platform.

Online forums. People can discuss their mental health conditions and seek mental help from online forums (also called online communities). There are various forms of online forums, such as chat rooms, discussion rooms (recoveryourlife, endthislife). For example, Saleem et al. designed a psychological distress detection model on 512 discussion threads downloaded from an online forum for veterans 26 . Franz et al. used the text data from TeenHelp.org, an Internet support forum, to train a self-harm detection system 27 .

Electronic health records

EHRs, a rich source of secondary health care data, have been widely used to document patients’ historical medical records 28 . EHRs often contain several different data types, including patients’ profile information, medications, diagnosis history, images. In addition, most EHRs related to mental illness include clinical notes written in narrative form 29 . Therefore, it is appropriate to use NLP techniques to assist in disease diagnosis on EHRs datasets, such as suicide screening 30 , depressive disorder identification 31 , and mental condition prediction 32 .

Some work has been carried out to detect mental illness by interviewing users and then analyzing the linguistic information extracted from transcribed clinical interviews 33 , 34 . The main datasets include the DAIC-WoZ depression database 35 that involves transcriptions of 142 participants, the AViD-Corpus 36 with 48 participants, and the schizophrenic identification corpus 37 collected from 109 participants.

Screening surveys

In order to evaluate participants’ mental health conditions, some researchers post questionnaires for clinician-patient diagnosis of patients or self-measurement. After participants are asked to fill in a survey from crowd-sourcing platforms (like Crowd Flower, Amazon’s Mechanical Turk) or online platforms, the data is collected and labeled. There are different survey contents to measure different psychiatric symptoms. For depression, the PHQ-9 (Patient Health Questionnaire) 38 or Beck Depression Inventory (BDI) questionnaire 39 are widely used for assessing the severity of depressive symptoms. The Scale Center for Epidemiological Studies Depression Scale (CES-D) questionnaire 40 with 20 multiple-choice questions is also designed for testing depression. For suicide ideation, there are many questionnaires such as the Holmes-Rahe Social Readjustment Rating Scale (SRRS) 41 or the Depressive Symptom Inventory-Suicide Subscale (DSI-SS) 42 .

Narrative writing

There are other types of texts written for specific experiments, as well as narrative texts that are not published on social media platforms, which we classify as narrative writing. For example, in one study, children were asked to write a story about a time that they had a problem or fought with other people, where researchers then analyzed their personal narrative to detect ASD 43 . In addition, a case study on Greek poetry of the 20th century was carried out for predicting suicidal tendencies 44 .

Types of mental illness

There are many applications for the detection of different types of mental illness, where depression (45%) and suicide (20%) account for the largest proportion; stress, anorexia, eating disorders, PTSD, bipolar disorder, anxiety, ASD and schizophrenia have corresponding datasets and have been analyzed using NLP (Fig. 5 ). This shows that there is a demand for NLP technology in different mental illness detection applications.

figure 5

The chart depicts the percentages of different mental illness types based on their numbers.

The amount of datasets in English dominates (81%), followed by datasets in Chinese (10%), Arabic (1.5%). When using non-English language datasets, the main difference lies in the pre-processing pipline, such as word segmentation, sentence splitting and other language-dependent text processing, while the methods and model architectures are language-agnostic.

NLP methods used to extract data

Machine learning methods.

Traditional machine learning methods such as support vector machine (SVM), Adaptive Boosting (AdaBoost), Decision Trees, etc. have been used for NLP downstream tasks. Figure 3 shows that 59% of the methods used for mental illness detection are based on traditional machine learning, typically following a pipeline approach of data pre-processing, feature extraction, modeling, optimization, and evaluation.

In order to train a good ML model, it is important to select the main contributing features, which also help us to find the key predictors of illness. Table 2 shows an overview of commonly used features in machine learning. We further classify these features into linguistic features, statistical features, domain knowledge features, and other auxiliary features. The most frequently used features are mainly based on basic linguistic patterns (Part-of-Speech (POS) 45 , 46 , 47 , Bag-of-words (BoW) 48 , 49 , 50 , Linguistic Inquiry and Word Count (LIWC) 51 , 52 , 53 ) and statistics (n-gram 54 , 55 , 56 , term frequency-inverse document frequency (TF-IDF) 57 , 58 , 59 , length of sentences or passages 60 , 61 , 62 ) because these features can be easily obtained through text processing tools and are widely used in many NLP tasks. Furthermore, emotion and topic features have been shown empirically to be effective for mental illness detection 63 , 64 , 65 . Domain specific ontologies, dictionaries and social attributes in social networks also have the potential to improve accuracy 65 , 66 , 67 , 68 . Research conducted on social media data often leverages other auxiliary features to aid detection, such as social behavioral features 65 , 69 , user’s profile 70 , 71 , or time features 72 , 73 .

Machine learning models have been designed based on a combination of various extracted features. The majority of the papers based on machine learning methods used supervised learning, where they described one or more methods employed to detect mental illness: SVM 26 , 74 , 75 , 76 , 77 , Adaptive Boosting (AdaBoost) 71 , 78 , 79 , 80 , k-Nearest Neighbors (KNN) 38 , 81 , 82 , 83 , Decision Tree 84 , 85 , 86 , 87 , Random Forest 75 , 88 , 89 , 90 , Logistic Model Tree (LMT) 47 , 47 , 91 , 92 , Naive Bayes (NB) 64 , 86 , 93 , 94 , Logistic Regression 37 , 95 , 96 , 97 , XGBoost 38 , 55 , 98 , 99 , and some ensemble models combining several methods 75 , 100 , 101 , 102 . The advantage of such supervised learning lies in the model’s ability to learn patterns from labeled data, thus ensuring better performance. However, labeling the large amount of data at a high quality level is time-consuming and challenging, although there are methods that help reduce the human annotation burden 103 . Thus, we need to use other methods which do not rely on labeled data or need only a small amount of data to train a classifier.

Unsupervised learning methods to discover patterns from unlabeled data, such as clustering data 55 , 104 , 105 , or by using LDA topic model 27 . However, in most cases, we can apply these unsupervised models to extract additional features for developing supervised learning classifiers 56 , 85 , 106 , 107 . Across all papers, few papers 108 , 109 used semi-supervised learning (models trained from large unlabeled data as additional information), including statistical model ssToT (semi-supervised topic modeling over time) 108 and classic semi-supervised algorithms (YATSI 110 and LLGC 111 ).

Deep learning methods

As mentioned above, machine learning-based models rely heavily on feature engineering and feature extraction. Using deep learning frameworks allows models to capture valuable features automatically without feature engineering, which helps achieve notable improvements 112 . Advances in deep learning methods have brought breakthroughs in many fields including computer vision 113 , NLP 114 , and signal processing 115 . For the task of mental illness detection from text, deep learning techniques have recently attracted more attention and shown better performance compared to machine learning ones 116 .

Deep learning-based frameworks mainly contain two layers: an embedding layer and a classification layer. By using an embedding layer, the inputs are embedded from sparse one-hot encoded vectors (where only one member of a vector is ‘1’, all others are ‘0’, leading to the sparsity) into dense vectors which can preserve semantic and syntactic information such that deep learning models can be better trained 117 . There are many different embedding techniques, such as ELMo, GloVe word embedding 118 , word2vec 119 and contextual language encoder representations (e.g., bidirectional encoder representations from transformers (BERT) 120 and ALBERT[ 121 ).

According to the structures of different classification layer’s structures, we have divided the deep learning-based methods into the following categories for this review: convolutional neural networks (CNN)-based methods (17%), recurrent neural networks (RNN)-based methods (36%), transformer-based methods (17%) and hybrid-based methods (30%) that combine multiple neural networks with different structures, as shown in Table 3 .

CNN-based methods. The standard CNN structure is composed of a convolutional layer and a pooling layer, followed by a fully-connected layer. Some studies 122 , 123 , 124 , 125 , 126 , 127 utilized standard CNN to construct classification models, and combined other features such as LIWC, TF-IDF, BOW, and POS. In order to capture sentiment information, Rao et al. proposed a hierarchical MGL-CNN model based on CNN 128 . Lin et al. designed a CNN framework combined with a graph model to leverage tweet content and social interaction information 129 .

RNN-based methods . The architecture of RNNs allows previous outputs to be used as inputs, which is beneficial when using sequential data such as text. Generally, long short-term memory (LSTM) 130 and gated recurrent (GRU) 131 networks models that can deal with the vanishing gradient problem 132 of the traditional RNN are effectively used in NLP field. There are many studies (e.g., 133 , 134 ) based on LSTM or GRU, and some of them 135 , 136 exploited an attention mechanism 137 to find significant word information from text. Some also used a hierarchical attention network based on LSTM or GRU structure to better exploit the different-level semantic information 138 , 139 .

Moreover, many other deep learning strategies are introduced, including transfer learning, multi-task learning, reinforcement learning and multiple instance learning (MIL). Rutowski et al. made use of transfer learning to pre-train a model on an open dataset, and the results illustrated the effectiveness of pre-training 140 , 141 . Ghosh et al. developed a deep multi-task method 142 that modeled emotion recognition as a primary task and depression detection as a secondary task. The experimental results showed that multi-task frameworks can improve the performance of all tasks when jointly learning. Reinforcement learning was also used in depression detection 143 , 144 to enable the model to pay more attention to useful information rather than noisy data by selecting indicator posts. MIL is a machine learning paradigm, which aims to learn features from bags’ labels of the training set instead of individual labels. Wongkoblap et al. used MIL to predict users with depression task 145 , 146 .

Transformer-based methods. Recently, transformer architectures 147 were able to solve long-range dependencies using attention and recurrence. Wang et al. proposed the C-Attention network 148 by using a transformer encoder block with multi-head self-attention and convolution processing. Zhang et al. also presented their TransformerRNN with multi-head self-attention 149 . Additionally, many researchers leveraged transformer-based pre-trained language representation models, including BERT 150 , 151 , DistilBERT 152 , Roberta 153 , ALBERT 150 , BioClinical BERT for clinical notes 31 , XLNET 154 , and GPT model 155 . The usage and development of these BERT-based models prove the potential value of large-scale pre-training models in the application of mental illness detection.

Hybrid-based methods. Some methods combining several neural networks for mental illness detection have been used. For examples, the hybrid frameworks of CNN and LSTM models 156 , 157 , 158 , 159 , 160 are able to obtain both local features and long-dependency features, which outperform the individual CNN or LSTM classifiers used individually. Sawhney et al. proposed STATENet 161 , a time-aware model, which contains an individual tweet transformer and a Plutchik-based emotion 162 transformer to jointly learn the linguistic and emotional patterns. Inspired by the improved performance of using sub-emotions representations 163 , Aragon et al. presented a deep emotion attention model 164 which consists of sub-emotion embedding, CNN, GRU as well as an attention mechanism, and Lara et al. also proposed Deep Bag of Sub-Emotions (DeepBose) model 165 . Furthermore, Sawhney et al. introduced the PHASE model 166 , which learns the chronological emotional progression of a user by a new time-sensitive emotion LSTM and also Hyperbolic Graph Convolution Networks 167 . It also learns the chronological emotional spectrum of a user by using BERT fine-tuned for emotions as well as a heterogeneous social network graph.

Evaluation metrics

Evaluation metrics are used to compare the performance of different models for mental illness detection tasks. Some tasks can be regarded as a classification problem, thus the most widely used standard evaluation metrics are Accuracy (AC), Precision (P), Recall (R), and F1-score (F1) 149 , 168 , 169 , 170 . Similarly, the area under the ROC curve (AUC-ROC) 60 , 171 , 172 is also used as a classification metric which can measure the true positive rate and false positive rate. In some studies, they can not only detect mental illness, but also score its severity 122 , 139 , 155 , 173 . Therefore, metrics of mean error (e.g., mean absolute error, mean square error, root mean squared error) 173 and other new metrics (e.g., graded precision, graded recall, average hit rate, average closeness rate, average difference between overall depression levels) 139 , 174 are sometimes needed to indicate the difference between the predicted severity and the actual severity in a dataset. Meanwhile, taking into account the timeliness of mental illness detection, where early detection is significant for early prevention, an error metric called early risk detection error was proposed 175 to measure the delay in decision.

Although promising results have been obtained using both machine and deep learning methods, several challenges remain for the mental illness detection task that require further research. Herein, we introduce some key challenges and future research directions:

Data volume and quality: Most of the methods covered in this review used supervised learning models. The success of these methods is attributed to the number of training datasets available. These training datasets often require human annotation, which is usually a time-consuming and expensive process. However, in the mental illness detection task, there are not enough annotated public datasets. For training reliable models, the quality of datasets is concerning. Some datasets have annotation bias because the annotators can not confirm a definitive action has taken place in relation to a disorder (e.g., if actual suicide has occurred) and can only label them within the constraints of their predefined annotation rules 9 . In addition, some imbalanced datasets have many negative instances (individuals without mental disorders), which is not conducive to training comprehensive and robust models. Therefore, it is important to explore how to train a detection model by using a small quantity of labeled training data or not using training data. Semi-supervised learning 176 incorporates few labeled data and large amounts of unlabeled data into the training process, which can be used to facilitate annotation 177 or improve classification performance when labeled data is scarce. Additionally, unsupervised methods can also be applied in mental disorders detection. For instance, unsupervised topic modeling 178 increases the explainability of results and aids the extraction of latent features for developing further supervised models. 179 , 180

Performance and instability: There are some causes of model instability, including class imbalance, noisy labels, and extremely long or extremely short text samples text. Performance is not robust when training on the datasets from different data sources due to diverse writing styles and semantic heterogeneity. Thus, the performance of some detection models is not good. With the advances of deep learning techniques, various learning techniques have emerged and accelerated NLP research, such as adversarial training 181 , contrastive learning 182 , joint learning 183 , reinforcement learning 184 and transfer learning 185 , which can also be utilized for the mental illness detection task. For example, pre-trained Transformer-based models can be transferred to anorexia detection in Spanish 186 , and reinforcement networks can be used to find the sentence that best reflects the mental state. Other emerging techniques like attention mechanism 187 , knowledge graph 188 , and commonsense reasoning 189 , can also be introduced for textual feature extraction. In addition, feature enrichment and data augmentation 190 are useful to achieve comparable results. For example, many studies use multi-modal data resources, such as image 191 , 192 , 193 , and audio 194 , 195 , 196 , which perform better than the single-modal text-based model.

Interpretability: The goal of representation learning for mental health is to understand the causes or explanatory factors of mental illness in order to boost detection performance and empower decision-making. The evaluation of a successful model does not only rely on performance, but also on its interpretability 197 , which is significant for guiding clinicians to understand not only what has been extracted from text but the reasoning underlying some prediction 198 , 199 , 200 . Deep learning-based methods achieve good performance by utilizing feature extraction and complex neural network structures for illness detection. Nevertheless, they are still treated as black boxes 201 and fail to explain the predictions. Therefore, in future work, the explainability of the deep learning models will become an important research direction.

Ethical considerations: It is of greater importance to discuss ethical concerns when using mental health-related textual data, since the privacy and security of personal data is significant and health data is particularly sensitive. During the research, the researchers should follow strict protocols similar to the guidelines 202 introduced by Bentan et al., to ensure the data is properly applied in healthcare research while protecting privacy to avoid further psychological distress. Furthermore, when using some publicly available data, researchers need to acquire ethical approvals from institutional review boards and human research ethics committees 203 , 204 .

There has been growing research interest in the detection of mental illness from text. Early detection of mental disorders is an important and effective way to improve mental health diagnosis. In our review, we report the latest research trends, cover different data sources and illness types, and summarize existing machine learning methods and deep learning methods used on this task.

We find that there are many applications for different data sources, mental illnesses, even languages, which shows the importance and value of the task. Our findings also indicate that deep learning methods now receive more attention and perform better than traditional machine learning methods.

We discuss some challenges and propose some future directions. In the future, the development of new methods including different learning strategies, novel deep learning paradigms, interpretable models and multi-modal methods will support mental illness detection, with an emphasis on interpretability being crucial for uptake of detection applications by clinicians.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was partially funded by the Alan Turing Institute and the H2020 EPHOR project, grant agreement No. 874703.

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Tianlin Zhang, Annika M. Schoene & Sophia Ananiadou

Department of Computer Science, Aalto University, Helsinki, Finland

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The Alan Turing Institute, London, UK

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Zhang, T., Schoene, A.M., Ji, S. et al. Natural language processing applied to mental illness detection: a narrative review. npj Digit. Med. 5 , 46 (2022). https://doi.org/10.1038/s41746-022-00589-7

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Mental Health Research Paper

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I. Introduction

Academic writing, editing, proofreading, and problem solving services, get 10% off with fall23 discount code, ii. the sociology of mental health: a brief history, a. the development of social epidemiology of mental health and disorders, iii. the study of mental health in contemporary sociology, a. the influence of other disciplines on the sociology of mental health, b. theoretical perspectives on mental health and disorder in sociology, c. defining a unique sociological approach to mental health and illness, 1. the stressor exposure perspective, 2. the social relationships perspective, 3. the societal reaction perspective, d. the influence of psychological models on the sociology of mental health and illness, e. methodological controversies, 1. measures of mental health and disorder, 2. measures of stressor exposure, f. the social epidemiology of mental disorders, 2. socioeconomic status, 4. marital status, iv. future directions in the sociology of mental health, a. comorbidity, b. mental health services and policy, c. better measures of stress exposure, d. better measures of social resources, e. the biological perspective on mental disorders, more mental health research papers:.

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This research paper describes the history, application, and development in sociology of the study of mental health, illness, and disorders. Mental health, mental illness, social and mental functioning, and its social indicators are a classic theme in the field of sociology. Emile Durkheim’s (1951) Suicide was a landmark study in both sociology and epidemiology, laying out a sociological course of research that remains an intellectual force in contemporary social science (Berkman and Glass 2000). The influence of the sociology of mental health and illness goes well beyond its sociological roots; its major theoretical perspectives interact with major research streams in psychiatry, psychology, anthropology, public health, and medicine (Aneshensel and Phelan 1999; Horwitz and Scheid 1999; Eaton 2001; Gallagher 2002; Cockerham 2005). The sociology of mental health also connects to numerous other fields in sociology, including general medical sociology, the sociology of aging, demography and biodemograpy, statistics, childhood studies, sociology of the life course, deviance, criminology, stratification, and studies of the quality of life.

Mental health, mental illness, and mental disorder are closely related but distinguishable concepts. Mental health refers to a state of well-being or alternatively, a state of mental normality, free of disorder or illness. Mental illness refers to a persistent state of mental abnormality. The term mental disorder is applied to a specific diagnosis of mental abnormality, such as depression, anxiety, schizophrenia, agoraphobia, mania or substance dependence.

In this research paper, the term sociology of mental health is used to refer to general theories and research that encompass the causes, development, and consequences of mental disorders and the state or symptoms of mental distress. The term also includes the study of personal and situational resources that preserve or restore the state of mental wellbeing. Sociologists who practice in the field of mental health examine a variety of outcomes and indicators of mental health as well as mental disorders.

The paper is organized into three sections: (1) a brief historical perspective on the study of mental health and illness in sociology; (2) the current state of research in the field, including its major themes and methodological problems; and (3) the future directions of the field. This research paper has four pervasive themes: (1) the interaction of the sociology of mental health and disorder with psychology, psychiatry, public health, and medicine; (2) the environmental perspective, which is the major contribution of the sociology to the mix of disciplines examining mental health in society; (3) the relationship between the study of mental health and studies of mental disorder; and (4) the emergence of the life course perspective as a dominant theoretical perspective in the sociology of mental health.

The topic of mental health has a venerable tradition in sociology. Durkheim’s classic work Suicide was translated into English in 1921, and it is still widely cited in the field. Durkheim’s work encouraged interest in the relationship of mental health and disorders with social structure, group membership, geographical location, and other indicators of social integration and organization. One of the most famous early applications of Durkheim’s perspective was Robert Merton’s (1938) work on social structure and anomie. Taken together, Durkheim and Merton introduced the influential idea that social systems can produce “stress” for individuals, who in turn may act in deviant or disordered ways (Cockerham 2005). Also applying Durkheim’s ideas, Faris and Dunham (1939) conducted a study of the distribution of schizophrenia in Chicago. Observing that people with schizophrenia clustered in high poverty areas, they argued that social isolation encouraged the development of symptoms characterizing schizophrenia.

Although Merton’s and Faris and Dunham’s theories no longer hold sway among contemporary sociologists of mental health, they are significant in their historical impact on the field. The organized field of the sociology of mental health grew out of the larger field of general medical sociology in the late 1930s and 1940s. Interest in mental illness and its causes were heightened by extraordinary events in the mid-twentieth century. The suffering of many ordinary Americans during the Great Depression, the discovery of psychiatric impairments among many World War II draftees, and the traumatic effects of combat on soldiers and civilians were powerful arguments for government support of efforts to mitigate mental illness (Kirk 1999).

The founding of the National Institutes of Mental Health (NIMH) in 1949 contributed to the development of medical sociology in general. The establishment of the Laboratory of Socio-Environmental Studies at NIMH in 1952 was a critical event in the development of studies of mental health in medical sociology. The sociologist John Clausen, who headed the laboratory, recruited and supported a number of sociologists who became leaders in the field, among them Melvin Kohn, Leonard Pearlin, Erving Goffman, and Morris Rosenberg (Kirk 1999). Using a strategy still dominant in behavioral science approaches to mental disorders, Clausen (1956) recruited social scientists from multiple disciplines as well as sociologists, stating that “the roles to be filled by sociologists within the mental health field call for collaboration with clinicians” (p. 47).

Throughout the 1950s, 1960s, and 1970s, NIMH was a major supporter of sociological and psychological research on mental health and illness. According to figures assembled by Kirk (1999), in 1976 more than 50 percent of NIMH research grants were to social, psychological, and behavioral scientists. A smaller proportion of grants were awarded to psychiatrists and physicians (a situation that no longer holds at NIMH).

Social epidemiology, sometimes labeled psychiatric epidemiology or social psychiatry (Gallagher 2002), is the discovery and documentation of the social and demographic distribution of mental disorders and health. The distribution of mental disorders can be documented via the study of medical records, mental hospital admissions, and surveys of the general population. Surveys in representative community populations, using clinically validated questions that identify and classify mental disorder symptoms by diagnostic categories, are the current tools used to estimate the prevalence of disorders (Cockerham 2005). The diagnostic estimates are then analyzed to determine their distribution by social and demographic group.

Hollingshead and Redlich (1958) (a sociologist and a psychiatrist) conducted an innovative study of mental disorders in New Haven, Connecticut, in which they compared mental illness inpatients and outpatients to a sample representative of the general community. Although not a study of prevalence the study had wide influence because of their findings that different types of mental disorder were distributed by social class, with more disorders among lower social class groups. The study also found that treatment for mental disorder varied by class. Because Hollingshead and Redlich’s study included only treated cases, however, they could not draw inferences about possible social causes of mental disorders.

The Midtown Manhattan Study in the 1950s (Srole et al. 1962) investigated the distribution of mental disorders using a random selection household survey design. The interview responses were rated by psychiatrists on the team. The findings from this study continue to shape social epidemiology today. Mental disorders were found to be more prevalent among respondents of lower socioeconomic status. Childhood poverty was linked to psychiatric impairment in adulthood (an early application of the life course perspective on mental health). Those who had mental disorders were less likely to be upwardly mobile. The investigators hypothesized that exposure to childhood and adult stressors played a key role in the distribution of mental disorders as well as mental health (Cockerham 2005). Many of these findings were replicated in a study of Nova Scotia communities (Leighton et al. 1963).

The environmental perspective on mental health was also advanced by studies led by social psychologists. Americans View Their Mental Health, two nationally representative interview studies conducted in 1956 and 1976 (Veroff, Douvan, and Kulka 1981), examined patterns over time in the contributions of the social environment to both positive and negative mental well-being as well as to patterns of help seeking for those who experienced mental distress.

A notable advance in the survey technology for measuring the prevalence of mental disorders and their social correlates was the Epidemiological Catchment Area (ECA) project, conducted by NIMH and five universities in the 1980s (Yale University, Johns Hopkins University, Washington University, Duke University, and the University of California at Los Angeles). A multidisciplinary team, including sociologists, psychiatrists, and psychologists developed new diagnosis instruments to detect mental disorders for use in the general population (Robins and Regier 1991). These diagnostic instruments, derived from the third version of the Diagnostic and Statistical Manual of the American Psychiatric Association (DSMIII), were coupled with interviews that measured environmental factors, social class, race, ethnicity, stressors, social relationships, and other factors believed to correlate with the risk of developing mental disorders.

The separate samples for the ECA studies, however, were not representative of the entire population of the United States. In 1990 through 1992, NIMH funded the first national survey of mental disorders in the general U.S. population (n = 8,068), the National Comorbidity Survey (NCS; Kessler and Zhao 1999). The investigators updated the interview diagnostic measures to reflect those recently developed by the American Psychiatric Association and the World Health Organization (Kessler et al. 1994). Along with diagnostic measures of depression, mania, anxiety, substance abuse, phobias, posttraumatic stress disorder, and other mood and psychotic disorders, the NCS interviews included measures of environmental factors, personality, childhood conditions, physical health, and mental health care utilization. NCS investigated the concept of comorbidity, which is defined as the occurrence of more than one type of mental disorder in an individual.

The NCS has been widely emulated and expanded. A version of the NCS was also conducted in Canada. NIMH also funded a series of replications of the NCS in 2000 to 2003 (Kessler et al. 2005), and the method has been extended to studying mental health and illness in children. The World Health Association is currently coordinating international replications of the NCS ( http://www.hcp.med.harvard.edu/ncs/ ).

As the foregoing brief historical overview shows, the study of mental health in sociology has been influenced by multiple disciplines. It is also host to a number of competing theoretical perspectives. The most widely discussed is the tension among medical, environmental, and societal reaction perspectives on the causes, consequences, and appropriate treatment of mental disorders. As a consequence of the host of influences on the field, there is considerable disagreement over the measurement of basic concepts in research, including how to define mental health and disorders (Kessler and Zhao 1999), environmental factors such as stressors, location, and socioeconomic status (Wheaton 1999); and social consequences such as disability, labeling, and social isolation (Horwitz and Scheid 1999; Pillemer et al. 2000). In addition, there is considerable creative tension between those who concentrate on establishing the incidence and prevalence of mental disorders and those who focus more on the correlates of mental health and mental illness (Mirowsky and Ross 2002, 2004). Finally, there is considerable research on the use of mental health services and on mental health policy.

As Clausen (1956) prophetically foresaw, sociologists who specialize in mental health frequently collaborate with those in other disciplines, such as developmental and social psychology, psychiatry, epidemiology, economics (Aneshensel and Phelan 1999; Gallagher 2002), and increasingly biology (Shanahan and Hofer 2005). The National Institutes of Health has encouraged and continues to encourage multidisciplinary approaches to the study of mental illness and disorders. Psychiatrists and clinical psychologists lay claim to the definitions of mental illness and disorder through the continuing revisions of the Diagnostic and Statistical Manual Mental Disorders, currently in its fourth edition (American Psychiatric Association 2000), as well as to measurements of mental distress (Radloff 1977), quality of life (Veroff et al. 1981), and social relationships and support (Cohen, Underwood, and Gottlieb 2000). Sociologists who study mental health compete for federal funds and intellectual prestige with those from other disciplines.

The presence of sociologists in interdisciplinary efforts to understand the causes, course, and consequences of mental illness and disorders is a positive situation; the influence of the sociology of mental health on other disciplines is tangible. A negative aspect of the interdisciplinary effort is that the sociology of mental health is sometimes viewed as isolated from the general field of sociology (Aneshensel and Phelan 1999). This perception may be exacerbated by the employment of sociologists of mental health (and other medical sociologists) in academic units other than Sociology departments. Members of the Sociology of Mental Health section of the American Sociological Association are employed in medical schools, schools of public health, schools of social work, and departments of human development. When theories of cause and measures of critical outcomes are shared with other disciplines, the question arises: What is the unique contribution of sociology to the study of mental health and illness? The answer to this question is pressing as there are calls for proposals that contribute to “the development, enhancement, and assembly of new data sets from existing data” and for research “that combines diverse levels of analysis” from national research and review bodies (National Institutes of Health 2004) as well as for research that examines the causes of health differences by socioeconomic status and behavioral risk factors across the life course (National Research Council 2004).

Five major perspectives, and combinations of these perspectives, are used in the contemporary sociology of mental health. The five major perspectives are (1) the medical model, (2) the environmental perspective, (3) the social psychological perspective, (4) societal reaction (or labeling), and (5) the life course perspective. The medical model views mental disorders as diseases and prescribes medical treatment as the appropriate cure. The environmental perspective asserts that factors such as social class, race, ethnicity, gender, urban location, and exposure to stressors may cause and most certainly shape risks for mental disorder. The social psychological perspective contributes insight into the social and relational factors that provide resources for adjusting to environmental stressors and restoring mental health and well-being. The social reaction perspective argues that mental illness emerges from social strain processes that produce deviance. The life course perspective views mental health and mental disorder as resulting from the accumulation of environmental stressors and exposures across the lifetime, in interaction with developmental and personal factors such as family structure, personality, and even genetic endowment. Researchers in the sociology of mental health often combine one or more of these perspectives in their research, with the life course perspective now generally seen as an emerging unifying paradigm (George 1999).

Although there is constant interaction between the mental health disciplines, several recent analyses of the state of theory in the sociology of mental health in the late twentieth century indicate the emergence of a distinct sociological approach. Horwitz and Scheid (1999) outlined two major approaches in the study of the sociology of mental health and illness. These two approaches are: (1) the social contexts producing or shaping mental health and disorder and (2) the recognition, treatment, and policy response to mental illness and disorder. In the same volume, Thoits (1999) described three major approaches that uniquely characterize the sociology of mental health: (1) stress exposure (a subset of the social context approach described by Horwitz and Scheid); (2) structural strain theory, which derives from Merton (1938); and (3) societal reaction, or labeling theory. Aneshensel and Phelan (1999) argue that the distinguishing issue in the sociological approach to mental illness is attention to how social stratification produces the unequal distribution of both disorders and mental health.

Aneshensel and Phelan also argue that a major challenge to the sociological approach to mental disorders is the debate between social causation and social selection explanations for the relationship between mental disorders and social class. The social selection approach hypothesizes that the reason there are more mental disorders in the lower economic class is because those with mental disorders are downwardly mobile economically or are unable to be upwardly mobile. This debate has many implications for interpreting how social stratification is linked to mental disorders and health (e.g., Miech et al. 1999).

The sociological approach also provides unique insight into the serious social consequences for those who have mental disorders, including socioeconomic success. The sociological approach also contributes research on the social factors that influence how institutions and individuals recognize when someone is mentally ill, how individuals are treated and how that treatment varies by social class, gender, and race, and who is more likely to use mental health care (e.g., Phelan et al. 2000).

The application of the sociological approach to mental health generates considerable empirical work that focuses on economic and other types of social stratification as determinants of mental health and mental disorder. This work is concentrated in research on stressor exposure, social relationships, and societal reaction to mental disorders.

The social context approach is a set of perspectives; the most well-known and applied outside the field of the sociology of mental health is the stress exposure perspective, which assumes that a combination or accumulation of stressors and difficulties can cause an onset of mental disorder. This perspective (Brown and Harris 1978; Dohrenwend et al. 1978), dominant in sociology, focuses on the level of change or threat posed by external events, and more recently, on the potential for chronic, unresolved stressors to threaten physical and mental health (Wheaton 1999).

Building on the strong history of social epidemiology in the field, the major assumption of this approach is that differential exposure to stressors by social class or social location is largely determined by social inequalities. In turn, the effects of prolonged stress exposure may perpetuate social inequality through the development of mental illness or disorder in disadvantaged populations (Pearlin et al. 2005). The latter point is more controversial (and in general less well developed theoretically); however the emerging life course or human developmental approach to the accumulation of disadvantage derives in some part from the stress exposure perspective (George 1999). The life course approach assumes that there is an accumulation of the negative effects of differential stressor exposure across life that perpetuates and magnifies inequalities and that many of these processes originate in childhood (e.g., McLeod and Kaiser 2004; McLeod and Nonnemaker 2000). A related stress exposure approach is stress diathesis, which assumes that stress exposure causes disorder only when there is a latent vulnerability (Eaton 2001). The diathesis approach is widely applied in psychiatric research on mental disorders.

Horwitz and Scheid (1999) add that in addition to stressor exposure, resources to help counter the negative impact of stressor exposure or to avoid stressor exposure also are differentially distributed by social class and location. The major types of social resources that vary by social class are (1) social integration, usually measured as access to meaningful and productive social roles (e.g., Pillemer et al. 2000); (2) social network characteristics (Turner and Turner 1999); (3) family structure (e.g., Turner, Sorenson, and Turner 2000); (4) received and perceived social support (Wethington and Kessler 1986); and (5) coping choices and styles (Pearlin and Schooler 1978; Pearlin et al. 1981). Thoits (1999) has pointed out that this approach, although distinct from the stressor exposure perspective, relies on stress exposure as a mechanism to activate the protective factors.

In an overview of the sociology of mental health, Thoits (1999) argued that there is no strong evidence that labeling or other societal reaction processes produce mental illness. However, the societal reaction perspective does provide an insight into social biases against those who display symptoms of mental disorder, which are often viewed as socially deviant. Aneshensel and Phelan (1999) concluded that there is a consensus among sociologists of mental health that mental disorders are objective entities and are not completely a product of social constructions. The strongest evidence for this conclusion is that symptoms of mental disorders are observed in all societies, although there are cultural variations in the ways that such symptoms are described and diagnosed.

A difficulty with this position for sociologists of mental health is that it implies there is widespread acceptance of the medical model, which can make theoretical interaction with other streams of sociology (e.g., the sociology of deviance) more contentious. Studies of the etiology of mental disorders in the population no longer routinely employ a deviance perspective. The stressor exposure model also applies a variation of the dose-response paradigm widely used in medical research. This acceptance of a variation of the medical model remains controversial and is probably related to the distance perceived between the sociology of mental health and the more mainstream sociology of stratification.

Yet another tension exists between opposing explanations of what causes social stratification in the distributions of mental disorders. On one side is the belief that routine functioning of society produces some of this stratification, as for example gender differences in the distribution of different types of disorders (Rosenfield 1999). In this view, mental distress and mental disorders can be produced by normal social processes such as gender role socialization. The stress exposure perspective, on the other hand, assumes that abnormal circumstances and events produce mental disorders and distress (Almeida and Kessler 1998). These two views are not necessarily impossible to resolve, but they continue to produce theoretical tensions.

Another factor producing distance between the sociology of mental health and the general field of sociology is the influence of social psychological theories on the field. As psychology has incorporated facets of the stress exposure perspective, sociologists of mental health have adopted ideas from social and developmental psychology on social support and relationships, coping, and life course development. An influential psychological perspective, the process of appraisal and coping, was developed by Lazarus and Folkman (1984), updated by Lazarus (1999), and has been further elaborated by Folkman and Moskowitz (2004). This perspective, dominant in the field of psychology, has emphasized how individual differences in perceptions of external stressors affect mental health. The focus of appraisal researchers on emotions as motivation for appraisal suggests commonality with biological research on emotion (Massey 2002). The theory of appraisal has been widely cited by sociologists who examine the impact of events on mental health (e.g.,Wethington and Kessler 1986).

The life course perspective (Elder 1974), now widely applied in the sociology of mental health (e.g., Wheaton and Clarke 2003; McLeod and Kaiser 2004), traces many of its components to the ecological perspective on human development pioneered by the developmental psychologist Urie Bronfenbrenner (1979). The life course perspective theorizes that developmental trajectories, developmental or socially normative timing of the stressor, and the accumulation of stressor exposure and resistance factors shape reaction to stressors (Elder, George, and Shanahan 1996). In the last decade, the life course perspective on stress accumulation has also been applied by psychologists, clinical psychologists, and neuroscientists (e.g., Singer and Ryff 1999; McEwen 2002; Repetti, Taylor, and Seeman 2002). Neuroscientists McEwen and Stellar (1993) have developed the concept of allostatic load which describes physiological mechanisms for the accumulated effects of past adaptation to stressors on health. Allostatic load is currently being adapted by sociologists to use in studies of stressor exposure across the life course and its relationship to mental health and disorder (Shanahan, Hofer and Shanahan 2003; Shanahan and Hofer 2005).

Sociological and psychological research streams on the relationship between stressor exposure and mental health are converging through collaborative efforts that examine the impact of stressor accumulation along the individual life course (Elder et al. 1996; Singer et al. 1998). A serious problem, however, is that most measures of stressor exposure available to researchers focus on recent exposures rather than the interactions of different types of stressor exposure over the long term; the majority of stressor exposure measures used in research are simple counts or sums of life events occurring over a short period of time (Wheaton 1999). Investigating the relationships between stressors over time and their combined associations with mental health and well-being is an important strategy for examining the impact of stressors over the life course (George 1999).

Issues of causality and theoretical approach are controversial in the field. Given the complexity and controversies in the sociology of mental health and illness, it is not surprising that one of the critical areas of the field is measurement. The two most disputed areas involve the measurement of outcomes and the measurement of stressor exposure.

The controversy begins with the outcomes. There is an increasing consensus that positive mental health and wellbeing is not just the absence of mental illness or disorder (Keyes 2002). There is also a controversy over whether dichotomous diagnoses of psychiatric disorder should be a proper outcome for sociological inquiry, in contrast to scales of distress symptoms (Kessler 2002; Mirowsky and Ross 2002).

Research diagnostic measures of mental disorder are controversial on many dimensions. Wakefield (1999) criticized the diagnostic measures used in the Epidemiological Catchment Area and National Comorbodity Studies for overestimating the prevalence of lifetime mental disorder in the United States. The NCS estimated that one-half of all Americans will suffer from a mental disorder over their lifetime (Kessler et al. 1994). A recent reanalysis of the NCS (Narrow et al. 2002), applying a standard of clinical seriousness based on other questions available in the survey, reduced the lifetime prevalence estimates significantly to 32 percent lifetime prevalence.

Another issue of controversy is whether a dichotomous outcome measure of disorder, one either has the disorder or not, misses levels of distress or poor social functioning that indicate considerable mental suffering (Kessler 2002; Mirowsky and Ross 2002). Persistent or recurring symptoms of sleeplessness, fatigue, sadness, loneliness, lack of appetite, and loss of interest in things in response to chronic stressors or unexpected life events can be unpleasant and disabling even if the sufferer does not show all of the symptoms of depression required for a diagnosis. The high threshold required for a diagnosis of disorder may understate emotional responses to events in the population at large. Whereas mental disorders may be relatively uncommon, symptoms of distress in response to life events are commonly observed and may indicate the presence of social dysfunction and strain in ways that surveys of mental disorders do not.

Measures of stressor exposure are particularly problematic in the sociology of mental health (Wheaton 1999). A complicating factor is that other mental health disciplines enforce higher standards of precision in measurement than does sociology. In addition, the majority of studies using stressor exposure measures do not account for any interaction between combinations of particular types of stressors. Applying the life course perspective model on mental health would ultimately require more sophisticated measures on how stressors combine and interact across time.

Both the biomedical and sociological streams of research on stress processes share an interest in environmental triggers of distress (Selye 1956). Following Selye, early stress researchers applied Selye’s assumption that all environmental threats activated the same or similar physiological response, using sums of exposures to different types of stressful events (Turner and Wheaton 1995). Almost immediately, sociologists and other social researchers modified this assumption, finding that more explicit and comprehensive measurement of the characteristics of stressors often increased the amount of variance explained in the mental health outcome. These measures included the estimated average “magnitude of change” scores in Social Readjustment Rating Scale (the SRRS: Holmes and Rahe 1967) and the Psychiatric Epidemiology Research Interview for Life Events (the PERI; Dohrenwend et al. 1978). Furthermore, it became clear that other characteristics of stressors, such as their type, timing, duration, severity, unexpectedness, controllability and impacts on other aspects of life make significant contributions to the stress response and mental health outcome (e.g., Brown and Harris 1978, 1989; Pearlin and Schooler 1978; Wethington, Brown, and Kessler 1995).

The stress exposure model is evolving to model the dynamic, continuous adaptation to stressors over time (e.g., Heckhausen and Schulz 1995; Lazarus 1999; Folkman and Moskowitz 2004). Sociologists have developed measures of chronic stress exposure (Pearlin and Schooler 1978) and exposure to stressors and hassles on a daily basis (Almeida, Wethington, and Kessler 2002). Researchers debate the relative reliability and validity of self-report checklist and interview measures of life events that include detailed probes that enable investigators to rate the severity of life events (Wheaton 1999). Most recently, psychologists have contributed to understanding variations in the relationships of different types of stressors (social loss vs. trauma and chronic vs. acute stressor exposure), to immune system function and cortisol activity (e.g., Dickerson and Kemeny 2004; Segerstrom and Miller 2004). Sociologists are now considering the potential for using measures of physiological activity (e.g., cortisol measurement) in their studies (Shanahan et al. 2003).

Applying the life course perspective to studying mental disorders and health over time has led to concern about the reliability and validity of retrospective measures of stressor exposure (Wethington et al. 1995; Wheaton 1999). Empirical research on memory for life events over a relatively short recall period is reassuring; most severe events can be recalled quite well over a 12-month retrospective period (Kessler and Wethington 1991). Serious concerns remain about longer retrospective recall periods. This concern is partially mitigated by the development of life history calendar methods, visual memory aids that can be used in interviews to enhance memory for life events (Freedman et al. 1988).

Despite the complexity of measurement, sociologists have pioneered the study of psychiatric sociology, or the epidemiology of mental disorders. The recent advances of measurement in the ECA and NCS studies have produced measures of outcomes that are scientifically accepted across disciplines (Cockerham 2005). These studies have also provided critical data on the use of mental health services by those who suffer from significant disorders and have had a major influence on other fields of study. The major epidemiological research questions have focused around the distribution of mental disorders and illnesses by social factors, including gender, socioeconomic status, marital status, race, and ethnicity. There is some, but more limited work, on factors such as ethnicity, migration, and location.

There is dispute whether the overall rate of mental disorders and illnesses differs by gender. The consensus before the publication of national data from the NCS was that men and women did not differ overall in rates of mental disorders; rather, different types of disorders are distributed differently. Women are more likely to report depressed affect and depressive disorders. Men, in turn, are more likely to report alcohol and drug disorders, violent behavior, and other indicators of acting out. Major psychoses such as schizophrenia and bipolar disorder are not distributed unequally by gender. There is now accumulating evidence that women are also more likely to report anxiety disorders (Kessler et al. 1994, 2005), which would mean that women are overall more likely to have mental disorders. Although there is continuing interest among biological and medical scientists to find a biological cause for women’s higher rates of some disorders, particularly depression, among sociologists social cause explanations still hold sway (e.g., Rosenfield 1999).

One of the most consistent findings in the epidemiology of mental disorders is that those of lower socioeconomic status are more likely to develop mental disorders (Cockerham 2005; Gallagher 2002). This general finding was confirmed by the NCS (Kessler and Zhao 1999). There is evidence, however, that those of higher statuses are more likely to suffer from affective disorders; the overrepresentation of mental disorders is due to higher rates of schizophrenia and some personality disorders among those of lower socioeconomic status.

Among sociologists of mental health, social causation theories continue to dominate, but more attention is being given to selection processes, especially the impact of mental disorders on upward economic mobility (e.g., Miech et al. 1999). Researchers who apply the life course perspective often study selection and economic mobility processes directly, most particularly those processes that affect educational attainment in early adulthood (e.g., McLeod and Kaiser 2004).

There remains considerable controversy in the literature whether members of racial minority groups report higher rates of mental disorder than majority racial groups. Given the relationship of socioeconomic status to mental health and disorders, it is logical to predict that rates of mental disorder in African Americans would be higher than the rates among white Americans because of the average lower socioeconomic status of blacks. Such a pattern would also reflect the additional burden of discrimination and prejudice and the impact such burdens have on mental well-being (Kessler, Mickelson, and Williams 1999).

The pattern of racial and ethnic differences, however, is more complex. For example, an analysis of risk and persistence of mental disorders among U.S. ethnic groups (Breslau et al. 2005) found that Hispanics reported lower lifetime prevalence of substance use disorders than whites, and that blacks reported lower lifetime prevalence of mood (depression or mania), anxiety, and substance use disorders. However, Hispanics were more likely to report persistent mood disorders (defined as recurrence of a past disorder), and blacks were more likely to report persistent mood and anxiety disorders. Research is needed on the factors that mitigate the impact of stressors on mental health of minority groups. Other researchers call for more attention to how mental disorders are measured and diagnosed in African Americans and other minority groups (e.g., Neighbors et al. 2003).

Although there is some evidence that pattern of mental distress by marital status may be changing as cohabitation becomes more socially accepted, the consensus still holds that married people are in better mental health and report fewer mental disorders than those who are not currently married. New research (Umberson and Williams 1999) points to the quality of the marital relationship as critical to mental well-being and health; those in unsatisfying or high-conflict marriages report poor mental health. Divorce is associated with poorer mental health over time, particularly among those who did not initiate the divorce.

Evidence such as that noted above is taken to mean that marriage confers benefits on mental health and may provide some protection against mental illness. Umberson and Williams (1999) note, however, that relatively little research has been done that has pitted the benefits of marriage perspective directly against the alternative social selection perspective that those who have mental disorders are less likely to marry or to remain married. Forthofer et al. (1996) estimated the relationship of age of onset of mental disorder on the probability of subsequent marriage. They found that those who have disorders are less likely to be married and when they marry have a higher risk of divorce. Unfortunately, studies that examine both social causation and social selection perspectives on marital status and mental health remain relatively rare, most likely because of the absence of satisfactory longitudinal data that can be used to address this issue.

One of the tensions in the sociology of mental health and illness is the interdisciplinary orientation of the field. Concepts are freely borrowed along the border of sociology and psychiatry/psychology. Much work is applied, or meant to be applied, to issues of importance to social policy, such as the social costs of untreated mental disorders. The life course perspective (Elder et al. 1996) is changing how research is done and how questions are being asked. New directions in the field include (1) a focus on comorbidity and severity of illness and its social impact, (2) the need for a closer connection between epidemiology and research on mental health services and policy, (3) the press to develop better measures of stressor exposure, (4) demand for more sophisticated measures and analyses of social resources, and (5) and the challenge of biological research on the stress process to the sociological study of mental health.

The study of comorbidity of mental disorders in people has transformed some aspects of the sociology of mental health. First, the documentation of comorbidity has influenced sociologists in the field to accept that mental illness is an objective reality. Second, it has become clear that those who are comorbid for multiple disorders are severely disabled in many important life roles. Their progress through life resembles the life path of “social selection.” Third, the acceptance that mental disorders are real physical entities, and the evidence for comorbidity are challenges to the environmental perspective on mental disorders. It is likely that those who have mental disorders attract or create stressor exposure (Eaton 2001). Thus, one major direction for sociological research in the future might be an emphasis on mental disorders as predictors, rather than outcomes, of social functioning and processes.

When reviewing the state of the sociology of mental health, Horwitz and Scheid (1999) observed that research on the social contexts of mental disorder and research on mental health services do not intersect all that much. They believed that this is because the two fields of research operate on different levels of analysis, one at the individual level and the other at the social or institutional level. A challenge for future research is to connect these two levels of analysis. Research on the social epidemiology of mental health and illness can inform organizations at all levels about the costs of untreated mental disorders to organizations and society in general.

As Wheaton (1999) observed, the social stress model requires considerable new development. This research paper has pointed out a number of methodological difficulties in measuring stressor exposure and the lack of fit between the most widely used measures of stressor exposure and the newly emerging life course perspective. Another advance would come through more detailed studies of how stressors are distributed in the population at large. Does the uneven distribution of stressors in the population “explain” the negative mental health outcomes for some groups? More research is needed in this area, ideally from the life course perspective, using longitudinal samples.

There is also a need for more research on the social distribution of resources that mitigate the impact of environmental challenges and stresses. Reviews of research on social support and social integration (e.g., Berkman and Glass 2000; Cohen et al. 2000; Pillemer et al. 2000) point out deficiencies in current measures of these resources. Do minority groups gain extra protection by asserting their identity and uniqueness? What is the social distribution of protective social resources? Do differences in distribution explain group differences in mental health?

The sociology of mental health is faced with a new challenge from the field of neuroscience. This research tends to be favored by federal funding agencies because of beliefs that neuroscience can lead to the discovery of new cures or therapeutic approaches to mental disorders. Neuroscience and its measurement equipment such as functional magnetic resonance imaging (fMRI) and cortisol sampling have the cachet of basic or “bench” science, while the observational and epidemiological approach of sociology is being portrayed as lower-quality science. However, the rise of neuroscience in research on mental disorders does not necessarily mean that social causes are irrelevant. The power of the new neuroscience of mental disorders is that it assumes there is an interaction between social factors and biological processes (McEwen 2002).

Yet there are serious impediments to the integration of sociological and biological research. One formidable impediment in sociology is the assumption that the biological perspective would reduce the entire stress process to individual differences in physical response, thus making environmental causation moot. Another impediment is that sociologists do not yet fully appreciate how much the biological approach to stress already incorporates measures of social context and stressors in studying adjustment to stressful events and situations (Singer and Ryff 1999). Sociologists (e.g., Pearlin et al. 1981) have long pointed out that the process of adjusting to stressors is a critical component of sociological and social psychological theories of the stress process (Thoits 1995). Thus, another challenge to sociologists of mental health is to incorporate techniques and measures that will powerfully represent the social context in multidisciplinary studies of mental health and mental disorders.

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