Building capacity in mental health care in low- and middle-income countries by training primary care physicians using the mhGAP: a randomized controlled trial
Bibliographic record
Abstract
To address the rise in mental health conditions in Tunisia, a training based on the Mental Health Gap Action Programme (mhGAP) Intervention Guide (IG) was offered to primary care physicians (PCPs) working in the Greater Tunis area. Non-specialists (such as PCPs)' training is an internationally supported way to target untreated mental health symptoms. We aimed to evaluate the programme's impact on PCPs' mental health knowledge, attitudes, self-efficacy and self-reported practice, immediately following and 18 months after training. We conducted an exploratory trial with a combination of designs: a pretest-posttest control group design and a one-group pretest-posttest design were used to assess the training's short-term impact; and a repeated measures design was used to assess the training's long-term impact. The former relied on a delayed-intervention strategy: participants assigned to the control group (Group 2) received the training after the intervention group (Group 1). The intervention consisted of a weekly mhGAP-based training session (totalling 6 weeks), comprising lectures, discussions, role plays and a support session offered by trainers. Data were collected at baseline, following Group 1's training, following Group 2's training and 18 months after training. Descriptive, bivariate and ANOVA analyses were conducted. Overall, 112 PCPs were randomized to either Group 1 (n = 52) or Group 2 (n = 60). The training had a statistically significant short-term impact on mental health knowledge, attitudes and self-efficacy scores but not on self-reported practice. When comparing pre-training results and results 18 months after training, these changes were maintained. PCPs reported a decrease in referral rates to specialized services 18 months after training in comparison to pre-training. The mhGAP-based training might be useful to increase mental health knowledge and self-efficacy, and decrease reported referral rates and negative mental health attitudes among PCPs in Tunisia and other low- and middle-income countries. Future studies should examine relationships among these outcome variables.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".