‘The government cannot do it all alone’: realist analysis of the minutes of community health committee meetings in Nigeria
Bibliographic record
Abstract
Since the mid-1980s, the national health policy in Nigeria has sought to inspire community engagement in primary health care by bringing communities into partnership with service providers through community health committees. Using a realist approach to understand how and under what circumstances the committees function, we explored 581 meeting minutes from 129 committees across four states in Nigeria (Lagos, Benue, Nasarawa and Kaduna). We found that community health committees provide opportunities for improving the demand and supply of health care in their community. Committees demonstrate five modes of functioning: through meetings (as 'village square'), reaching out within their community (as 'community connectors'), lobbying governments for support (as 'government botherers'), inducing and augmenting government support (as 'back-up government') and taking control of health care in their community (as 'general overseers'). In performing these functions, community health committees operate within and through the existing social, cultural and religious structures of their community, thereby providing an opportunity for the health facility with which they are linked to be responsive to the needs and values of the community. But due to power asymmetries, committees have limited capacity to influence health facilities for improved performance, and governments for improved health service provision. This is perhaps because national guidelines are not clear on their accountability functions; they are not aware of the minimum standards of services to expect; and they have a limited sense of legitimacy in their relations with sub-national governments because they are established as the consequence of a national policy. Committees therefore tend to promote collective action for self-support more than collective action for demanding accountability. To function optimally, community health committees require national government or non-government organization mentoring and support; they need to be enshrined in law to bolster their sense of legitimacy; and they also require financial support to subsidise their operation costs especially in geographically large communities.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".