Long‐term impact of a mental health literacy resource applied by regular classroom teachers in a Canadian school cohort
Bibliographic record
Abstract
Application of evidence-based mental health literacy (MHL) curriculum resources by classroom teachers has been demonstrated to significantly improve knowledge and decrease stigma in the short term. AIMS: To report results that extend these positive findings for a period of one year. METHOD: In a naturalistic cohort study, 332 grade 9 students (ages 14-15) in a Canadian school district learned from an evidence-based curriculum resource (the Guide) applied by classroom teachers who trained in its use. Evaluations of knowledge and stigma were conducted before the Guide, immediately following the Guide delivery and at one-year follow-up. RESULTS: Students showed significant (p < .001) and substantial (d = 0.68 and 0.67) short-term and significant (p < .001) and substantial (d = 0.44 and 0.58) long-term improvements in knowledge and reductions in stigma. Significant stigma reduction was found among female students than male students, but no gender differences on knowledge were found at long-term follow-up. Educators showed significant and substantial short-term improvements in knowledge (p < .001; d = 1.03) and reductions in stigma (p < .05; d = 0.35). CONCLUSIONS: The Guide resource delivered by trained classroom teachers may have value in enhancing MHL outcomes for young people.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".