Mental health capacity building in Mali by training rural general practitioners and raising community awareness
Bibliographic record
Abstract
INTRODUCTION: despite the high prevalence and significant burden of mental disorders, they remain grossly under-diagnosed and undertreated. In low-income countries, such as Mali, integrating mental health services into primary care is the most viable way of closing the treatment gap. This program aimed to provide a mental health training intervention to rural general practitioners (GPs), to organize community awareness activities, and to evaluate the impact on mental health knowledge and through the number of new patients diagnosed with mental disorders and managed by these general practitioners. METHODS: a pre-test/post-test design and the monthly monitoring of the number of new patients diagnosed with mental disorders by the trained GPs were used to evaluate the effect of the training interventions (two face-to-face group training workshops followed by individual follow-up supervisions) and of the community awareness activities. RESULTS: the mean knowledge score of the 19 GPs who completed the initial 12-day group training raised from 24.6/100 at baseline, to 61.5/100 after training (p<0.001), a 150% increase. Among them, sixteen completed the second 6-day group training with a mean score increasing from 50.2/100 to 70.1/100 (p<0.001), a 39.6% improvement. Between July 2018 and June 2020, 2,396 new patients were diagnosed with a mental disorder by the 19 GPs who took part in the program. CONCLUSION: despite limited data regarding the effect of the community awareness component at this stage, the findings from this study suggest that the training intervention improved GPs' knowledge and skills, resulting in a significant number of new patients being identified and managed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".