The African Guide: One Year Impact and Outcomes from the Implementation of a School Mental Health Literacy Curriculum Resource in Tanzania
Bibliographic record
Abstract
little research is available. Schools are an ideal location in which to address mental health literacy. A Canadian school-based mental health literacy resource was adapted for application in sub-Saharan Africa called the African Guide (AG). The AG is a classroom ready curriculum resource addressing all aspects of mental health literacy. Herein we provide teacher reported activity impacts and MHL outcomes from the implementation of the AG in Tanzania. Following training, survey data addressing teacher reported AG impact and MHL outcomes was collected at three time points over a one year period. Over a period of one year, 32 teachers from 29 different schools reported that over: 4,600 students were taught MHL; 150 peer teachers were trained on the AG; 390 students approached teachers with a mental health concern; 450 students were referred to previously trained community care providers for diagnosis and treatment of Depression; and most students were considered to have demonstrated improved or very much improved knowledge, attitudes and help-seeking efficacy, with similar outcomes reported for teachers. Results of this study demonstrate a substantial positive impact on MHL related activities and outcomes for both students and teachers using the AG resource in Tanzania. Taken together with previously published research on enhancing MHL in both Malawi and Tanzania, if replicated in another setting, these results will provide additional support for the scale up of this intervention across sub-Saharan 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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".