Engaging culture and context in mhGAP implementation: fostering reflexive deliberation in practice
Bibliographic record
Abstract
In 2002, WHO launched the Mental Health Gap Action Programme (mhGAP) as a strategy to help member states scale up services to address the growing burden of mental, neurological and substance use disorders globally, especially in countries with limited resources. Since then, the mhGAP program has been widely implemented but also criticised for insufficient attention to cultural and social context and ethical issues. To address this issue and help overcome related barriers to scale-up, we outline a framework of questions exploring key cultural and ethical dimensions of mhGAP planning, adaptation, training, and implementation. This framework is meant to guide mhGAP activity taking place around the world. Our approach is informed by recent research on cultural formulation and adaptation, and aligned with key components of the WHO implementation research guide (Peters, D. H., Tran, N. T., & Adam, T. (2013). Implementation research in health: a practical guide. Implementation research in health: a practical guide.). The framework covers three broad domains: (1) Concepts of wellness and illness—how to examine cultural norms, knowledge, values and attitudes in relation to the “culture of the mhGAP”; (2) Systems of care—identifying formal and informal systems of care in the cultural context of practice.; and (3) Ethical space: examining issues related to power dynamics, communication, and decision-making. Systematic consideration of these issues can guide integration of cultural knowledge, structural competence, and ethics in implementation efforts.
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 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 | 0.197 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.016 | 0.063 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.008 | 0.045 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".