Successful Application of a Canadian Mental Health Curriculum Resource by Usual Classroom Teachers in Significantly and Sustainably Improving Student Mental Health Literacy
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the significant and substantive findings from a previous study of youth mental health literacy (MHL) could be replicated using the same methods in another population. METHOD: We examined the impact of a curriculum resource, the Mental Health and High School Curriculum Guide (The Guide), taught by usual classroom teachers on students' knowledge and attitudes related to mental health and mental illness in Canadian secondary schools. Survey data were collected before, immediately after, and 2 months after implementation of The Guide by teachers in usual classroom teaching. We conducted paired-sample t tests and calculated the Cohen d value to determine outcomes and impact of the curriculum resource application. RESULTS: One hundred fourteen students were matched for analysis of knowledge data and 112 students were matched for analysis of attitude data at pre-intervention, post-intervention, and 2-month follow-up time periods. Following classroom exposure to the curriculum resource, students' knowledge scores increased significantly and substantively, compared with baseline (P < 0.001, d = 1.11), and this was maintained at 2-month follow-up (P < 0.001, d = 0.91). Similar findings for attitude improvement were found (P < 0.001, d = 0.66), and this improvement was maintained at 2-month follow-up (P < 0.001, d = 0.52). CONCLUSIONS: These findings corroborate those from a previous study conducted in a different location. Taken together these results suggest a simple but effective approach to improving MHL in young people by embedding a classroom resource, delivered by usual classroom teachers in usual school settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".