Evaluating the Impacts of an Undergraduate Mental Health Literacy Course
Bibliographic record
Abstract
Mental health literacy is an important factor in caring for mental health; however, in the post-secondary student population, mental health literacy is reported to be low. To address these issues, in 2020, one of the authors [CZ] developed an undergraduate mental health literacy (MHL) course offered through the Faculty of Health, University of Waterloo, to undergraduate students in all Faculties. Building on promising early research conducted by two of the authors in 2021, we conducted three studies to evaluate the impacts of the fully online version of this MHL course. Study #1 was a pre-post study to examine knowledge and attitudes related to mental health literacy (n = 162). Study #2 was a one-month follow-up study to assess continued use of mental wellness strategies practiced during the course (n = 18), and study #3 was a content analysis of part one of the final reflection assignment of the course (n = 32). The pre-post study did not reveal any meaningful changes in knowledge and attitude from Time 1 to Time 2. However, the one-month follow-up study and the content analysis of the final reflection assignment showed multiple meaningful and positive shifts in knowledge, attitudes, and behaviours related to mental health, stigma, self-care, well-being, supporting others with mental health concerns, and the meaning of resilience. In addition, the content analysis revealed that students were sharing course resources with peers, friends, family members and with their community at large. In addition to guiding revisions to strengthen the MHL course under investigation, our findings are relevant to other MHL education initiatives in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".