Tailoring a training based on the <i>Mental Health Gap Action Programme (mhGAP) Intervention Guide (IG)</i> to Tunisia: process and relevant adaptations
Bibliographic record
Abstract
Background In order to make mental health services more accessible, the Tunisian Ministry of Health, in collaboration with the School of Public Health at the University of Montreal, the World Health Organization office in Tunisia and the Montreal World Health Organization-Pan American Health Organization Collaborating Center for Research and Training in Mental Health, implemented a training programme based on the Mental Health Gap Action Programme (mhGAP) Intervention Guide (IG) (version 1.0), developed by the World Health Organization. This article describes the phase prior to the implementation of the training, which was offered to general practitioners working in primary care settings in the Greater Tunis area of Tunisia. Methods The phase prior to implementation consisted of adapting the standard mhGAP-IG (version 1.0) to the local primary healthcare context. This adaptation process, an essential step before piloting the training, involved discussions with stakeholder groups, as well as field observations. Results Through the adaptation process, we were able to make changes to the standard training format and material. In addition, the process helped uncover systemic barriers to effective mental health care. Conclusions Targeting these barriers in addition to implementing a training programme may help reduce the mental health treatment gap, and promote implementation that is successful and sustainable.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".