Postsecondary Students' Information Needs and Pathways for Help with Stress, Anxiety, and Depression/Les Besoins D'information Des éTudiants Postsecondaires et Les Parcours Conçus Pour Réduire le Stress, L'anxiété, et la Dépression
Bibliographic record
Abstract
Mental disorders are common among young adults, with the prevalence of the most common disorders, such as anxiety and depression, reaching a peak between ages 18 and 24 (Kessler, 2007). When considering postsecondary students more specifically-where academic, financial, and interpersonal stressors compound the age-related risk factors-the prevalence of mental disorders may be even higher (Cooke, Bewick, Barkham, Bradley, & Audin, 2006). One national U.S. survey found that almost half of the students sampled met the DSM-IV criteria for at least one mental disorder in the previous year, including 12% for an anxiety disorder and 18% for a mood disorder (Blanco et ah, 2008). More recently, a North American survey of students indicated that more than 80% of respondents felt exhausted and overwhelmed, with nearly half reporting that they felt hopeless at some point in the past academic year (American College Health Association, 2013). In line with these findings, rates of suicidality are also high among postsecondary students, with one U.S. national survey indicating that more than half of students surveyed had considered suicide at some point in their lives, including an alarming 8% of undergraduates who reported at least one suicide attempt (Drum, Brownson, Burton Denmark, & Smith, 2009).Given the high levels of mental health concerns among postsecondary students, one might expect that mental health service utilization would be similarly high; however, this is not the case (Kiley, 2013). A national U.S. survey showed that fewer than half the students screening positive for mood or anxiety disorders reported receiving any mental health services during the preceding year (Eisenberg, Golberstein, & Gollust, 2007). Although there have been apparent increases in the willingness of postsecondary students to access campus mental health services (Hunt & Eisenberg, 2010), young adults are among the age cohorts least likely to seek help for their mental health problems (Statistics Canada, 2011; Wang et ah, 2005). In addition, when young adults do seek treatment, they may not be basing their decisions on complete information or seeking this information from reliable sources. As such, it is worthwhile to explore the ways in which mental health information and treatment resources can be made more accessible for this vulnerable cohort.Much of the information that young adults want or need about mental health problems and treatment options can be understood within the framework of mental health literacy, which may be defined as the extent to which individuals in need of treatment are able to recognize and identify their symptoms as a condition requiring access to mental health resources (Coles & Coleman, 2010). The role of mental health literacy in service accessibility is highlighted by a recent Australian study that found only 26% of students would seek help from a general practitioner and only 10% from a student counsellor should they experience a mental health problem (Reavley, McCann, & Jorm, 2012). Results from a U.K. survey of 3,000 young people aged 16-24 showed similar results (Klineberg, Biddle, Donovan, & Gunnell, 2011). In this study, participants were asked to identify if characters from a vignette had depression, and what they thought the characters would do in terms of seeking help. Interestingly, about one third of the participants who recognized severe mental health symptoms in the vignettes thought that the characters would do nothing about their mental health problems (Klineberg et al., 2011).In addition to low rates of mental health literacy, previous research has identified a number of psychological factors that are related to reduced rates of help-seeking, including lack of emotional openness (Hunt & Eisenberg, 2010), degree of symptom severity (Leahy et al., 2010; Wilson, 2010), and self-stigma (Eisenberg, Downs, Golberstein, & Zivin, 2009). Of these factors, self-stigma is of particular concern because of its pervasive nature and impact. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".