Co-creating systems change for mental health: a theory of change approach from the MeHPriC initiative in Lagos, Nigeria
Bibliographic record
Abstract
BACKGROUND: Integrating mental health services into primary health care (PHC) in low- and middle-income countries (LMICs) is a complex systems-change challenge that requires robust, contextually adapted frameworks. The Mental Health in Primary Care (MeHPriC) initiative in Lagos, Nigeria, aimed to scale up Mental Health Gap Action Programme (mhGAP) based task-sharing for depression, psychosis, and epilepsy. To guide this complex intervention, a participatory Theory of Change (ToC) approach was adopted as a planning, implementation, and governance tool. METHODS: Using a participatory action research design guided by the Consolidated Framework for Implementation Research (CFIR), the MeHPriC ToC was co-created over an 18-month period (2013-2014). The process involved three structured workshops, 36 stakeholder-specific consultations, and four technical working groups with over 150 participants from government, health facilities, and communities. A Community Knowledge, Attitudes, and Practices survey assessed community-level changes in mental health literacy and stigma. A mixed-methods evaluation was conducted (2014-2017) to assess implementation and clinical outcomes using the ToC as an analytical framework, with operational definitions established for key indicators. RESULTS: The participatory process produced a comprehensive, co-owned ToC map detailing causal pathways, assumptions, and indicators across community, facility, administrative, and state levels. Implementation outcomes included training 320 PHC workers, achieving 69.1% practice adoption and 79.6% fidelity to core protocols. This resulted in a 58.7% increase in mental health consultations and a 60.3% clinical recovery rate for depression. Community stigma remained at 20% post-intervention. A systematic analysis of implementation barriers and facilitators through CFIR domains showed distinct patterns within each domain, such as the need for cultural adaptations, involvement of religious leaders, and the use of hybrid supervision models. Key policy wins included integration of mental health indicators into the state Health Management Information System and establishment of dedicated budget lines for supervision. CONCLUSION: A participatory and empirically-refined ToC approach can serve as an effective governance and implementation framework for complex health system interventions in LMIC settings. The MeHPriC experience demonstrates that this methodology guides implementation to achieve positive clinical outcomes while fostering stakeholder alignment necessary for policy integration and long-term sustainability.
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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 | 0.022 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".