A step-wise community engagement and capacity building model prior to implementation of mhGAP-IG in a low- and middle-income country: a case study of Makueni County, Kenya
Bibliographic record
Abstract
BACKGROUND: The World Health Organization developed the Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) as guidelines for innovative utilization of available resources in low- and middle-income countries (LMICs) in order to accelerate the reduction of the mental health treatment gap. The mhGAP-IG calls for each country to contextualize the guide to their social, cultural and economic context. The objective of this paper is to describe a model for a stepwise approach for implementation of mhGAP-IG in a rural Kenyan setting using existing formal and informal community resources and health systems. METHODS: We conducted an analysis of mental health services in Makueni County, one of the 47 counties in Kenya, in order to understand the existing gaps and opportunities in a low-resource setting. We conducted stakeholder analysis and engagement through interactive dialogue in order for them to appreciate the importance of mental health to their communities. Through the process of participatory Theory of Change, the stakeholders gave their input on the process between the initiation and the end of the process for community mental health development, with the aim of achieving buy-in and collective ownership of the whole process. We adapted the mhGAP-IG to the local context and trained local human resources in skills necessary for the implementation of mhGAP-IG and for monitoring and evaluating the process using instruments with good psychometric properties that have been used in LMICs. RESULTS: We were able to demonstrate the feasibility of implementing the mhGAP-IG using existing and trained community human resources using a multi-stakeholder approach. We further demonstrated the feasibility to transit seamlessly from research to policy and practice uptake using our approach. CONCLUSIONS: An inclusive model for low resource settings is feasible and has the potential to bridge the gap between research, policy and practice. A major limitation of our study is that we did not engage a health economist from the beginning in order to determine the cost-effectiveness of our proposed model, occasioned by lack of resources to hire a suitable one.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".