Mental health literacy in post-secondary students
Bibliographic record
Abstract
Objectives: The transition from high school to college or university is an important time to enhance mental health literacy for young people. This study evaluated the second edition of a resource entitled Transitions , a comprehensive life-skills resource with embedded mental health information available in book, e-book and iPhone app formats for post-secondary students. Design: In this cross-sectional/one-off study, students’ opinions about the impact of the resource were gained through in-person and online surveying. Methods: The survey took place on a local university campus in Canada. Frequencies of responses and sex differences in answers were analysed using Predictive Analytics Software (PASW) 17. In total, 82 students from a large, Canadian research university completed the survey. Results: Reading Transitions (2nd edition) improved student knowledge about and decreased stigma towards mental health and mental illness and increased help-seeking efficacy. There were sex differences in response regarding discussion of the resource with others and help-seeking intentions. Conclusion: Given the positive results of this study conducted at a single university, the resource could potentially be valuable in other post-secondary settings as well.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".