Mental health challenges, treatment experiences, and care needs of post-secondary students: a cross-sectional mixed-methods study
Bibliographic record
Abstract
Abstract Background: Post-secondary students are at a greater risk of developing mental health problems than the general population. However, they present meagre rates of treatment-seeking behaviours. This elevated prevalence of mental health problems, particularly after the COVID-19 pandemic, can lead to distress, poor academic performance, and lower job prospects following the completion of their education. To address the needs of this population, it is important to understand students' perceptions of mental health and the barriers preventing or limiting their access to care. Methods: A broad-scoping online survey was publicly distributed to post-secondary students, collecting demographic, sociocultural, economic, and educational information while assessing various components of mental health. Results: In total, 448 students across post-secondary institutions in Ontario, Canada, responded to the survey. Over a third (n = 170; 38.6%) of respondents reported a formal mental health diagnosis. Depression and generalized anxiety disorder were the most commonly reported diagnoses. Most respondents felt that post-secondary students have poor mental health (n = 253; 60.5%) and inadequate coping strategies (n = 261; 62.4%). The most frequently reported barriers to care were financial (n = 214; 50.5%), long wait times (n = 202; 47.6%), insufficient resources (n = 165; 38.9%), time constraints (n = 148; 34.9%), stigma (n = 133; 31.4%), cultural barriers (n = 108; 25.5%), and past negative experiences with mental health care (n = 86; 20.3%). Most students felt their post-secondary institution needed to increase mental health resources (n = 306; 73.2%) and awareness (n = 231; 56.5%). Most students who had or were receiving care viewed in-person therapy as more helpful than online care. However, there was uncertainty about the helpfulness and accessibility of different forms of treatment, including online interventions. Conclusions: Lack of resources, barriers to care, and uncertainty surrounding interventions contribute to the low treatment-seeking behaviours observed in post-secondary students. The survey findings indicate that multiple upstream approaches, including formal mental health education, may address the varying needs of this critical population. Online mental health interventions may be a promising solution to accessibility issues.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".