It’s Time to Bring Mental Health Literacy Education into the Postsecondary Curriculum
Bibliographic record
Abstract
In the last twenty years, research on post-secondary students’ mental health and well-being has grown substantially, with a dramatic increase in publications over the past decade. Likewise, concerns about declining mental health on our campuses have risen; the mental well-being of postsecondary students is now widely recognized as a major public health issue. Over the last two decades, Canadian higher education has largely addressed these concerns by promoting mental health awareness through extracurricular means. Critically, a new movement towards mental health literacy has emerged across the nation: not just supplementary outreach, but education embedded into the curriculum. To put recommendations into practice, in 2020, one of the authors [CZ] developed and taught an undergraduate course on mental health literacy with a class of 106 students. In the first offering, we conducted a pre-post study to examine if this new course would be associated with changes in mental health knowledge, stigma, and help-seeking. Of the forty students who participated in the study, ten completed measures at both the start (T1) and the end of the course (T2). Within-subjects analyses showed that students made significant gains from T1 to T2, with a large effect size, in terms of attitudes toward seeking mental health services. Feedback on the course was very positive, both in students’ ratings and their comments. Looking ahead, student well-being will depend on how institutions approach and engage with mental health literacy. We recommend firmly integrating mental health literacy education into the post-secondary curriculum.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".