Considering culture, context and community in mhGAP implementation and training: challenges and recommendations from the field
Bibliographic record
Abstract
BACKGROUND: Major efforts are underway to improve access to mental health care in low- and middle-income countries (LMIC) including systematic training of non-specialized health professionals and other care providers to identify and help individuals with mental disorders. In many LMIC, this effort is guided by the mental health Gap Action Programme (mhGAP) established by the World Health Organization, and commonly centres around one tool in this program: the mhGAP-Intervention Guide. OBJECTIVE: To identify cultural and contextual challenges in mhGAP training and implementation and potential strategies for mitigation. METHOD: An informal consultative approach was used to analyze the authors' combined field experience in the practice of mhGAP implementation and training. We employed iterative thematic analysis to consolidate and refine lessons, challenges and recommendations through multiple drafts. Findings were organized into categories according to specific challenges, lessons learned and recommendations for future practice. We aimed to identify cross-cutting and recurrent issues. RESULTS: Based on intensive fieldwork experience with a focus on capacity building, we identify six major sets of challenges: (i) cultural differences in explanations of and attitudes toward mental disorder; (ii) the structure of the local health-care system; (iii) the level of supervision and support available post-training; (iv) the level of previous education, knowledge and skills of trainees; (v) the process of recruitment of trainees; and (vi) the larger socio-political context. Approaches to addressing these problems include: (1) cultural and contextual adaptation of training activities, (2) meaningful stakeholder and community engagement, and (3) processes that provide support to trainees, such as ongoing supervision and Communities of Practice. CONCLUSION: Contextual and cultural factors present major barriers to mhGAP implementation and sustainability of improved services. To enable trainees to effectively apply their local cultural knowledge, mhGAP training needs to: (1) address assumptions, biases and stigma associated with mental health symptoms and problems; (2) provide an explicit framework to guide the integration of cultural knowledge into assessment, treatment negotiation, and delivery; and (3) address the specific kinds of problems, modes of clinical presentations and social predicaments seen in the local population. Continued research is needed to assess the effectiveness these strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".