WHO Mental Health Gap Action Programme Intervention Guide (mhGAP-IG): the first pre-service training study
Bibliographic record
Abstract
BACKGROUND: Despite the increasing burden of mental, neurological, and substance use (MNS) disorders, a significant treatment gap for these disorders continues to exist across the world, and especially in low- and middle-income countries. To bridge the treatment gap, the World Health Organization developed and launched the Mental Health Gap Action Programme (mhGAP) and the mhGAP Intervention Guide (mhGAP-IG) to help train non-specialists to deliver care. Although the mhGAP-IG has been used in more than 100 countries for in-service training, its implementation in pre-service training, that is, training prior to entering caregiver roles, is very limited. AIM OF THE STUDY: The aim of this study was to collect and present information about the global experience of academic institutions that have integrated WHO's mhGAP-IG into pre-service training. METHODS: A descriptive cross-sectional study was conducted using an electronic questionnaire, from December 2018 to June 2019. RESULTS: Altogether, eleven academic institutions across nine countries (Mexico, Nigeria, Liberia, Sierra Leone, Somaliland, Armenia, Georgia, Ukraine and Kyrgyzstan) participated in this study. Five of the institutions have introduced the mhGAP-IG by revising existing curricula, three by developing new training programmes, and three have used both approaches. A lack of financial resources, a lack of support from institutional leadership, and resistance from some faculty members were the main obstacles to introducing this programme. Most of the institutions have used the mhGAP-IG to train medical students, while some have used it to train medical interns and residents (in neurology or family medicine) and nursing students. Use of the mhGAP-IG in pre-service training has led to improved knowledge and skills to manage mental health conditions. A majority of students and teaching instructors were highly satisfied with the mhGAP-IG. CONCLUSIONS: This study, for the first time, has collected evidence about the use of WHO's mhGAP-IG in pre-service training in several countries. It demonstrates that the mhGAP-IG can be successfully implemented to train a future cadre of medical doctors and health nurses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".