Improving Malawian teachers' mental health knowledge and attitudes: an integrated school mental health literacy approach
Bibliographic record
Abstract
BACKGROUND: Mental health literacy is foundational for mental health promotion, prevention, stigma reduction and care. Integrated school mental health literacy interventions may offer an effective and sustainable approach to enhancing mental health literacy for educators and students globally. METHODS: Through a Grand Challenges Canada funded initiative called 'An Integrated Approach to Addressing the Issue of Youth Depression in Malawi and Tanzania', we culturally adapted a previously demonstrated effective Canadian school mental health curriculum resource (the Guide) for use in Malawi, the African Guide: Malawi version (AGMv), and evaluated its impact on enhancing mental health literacy for educators (teachers and youth club leaders) in 35 schools and 15 out-of-school youth clubs in the central region of Malawi. The pre- and post-test study designs were used to assess mental health literacy - knowledge and attitudes - of 218 educators before and immediately following completion of a 3-day training programme on the use of the AGMv. RESULTS: = 0.79) pertaining to mental health literacy in study participants. There were no significant differences in outcomes related to sex or location. CONCLUSIONS: These positive results suggest that an approach that integrates mental health literacy into the existing school curriculum may be an effective, significant and sustainable method of enhancing mental health literacy for educators in Malawi. If these results are further found to be sustained over time, and demonstrated to be effective when extended to students, then this model may be a useful and widely applicable method for improving mental health literacy among both educators and students across Africa.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".