Community leaders' perspectives on linking formal and informal health providers in Nigerian urban slums: a qualitative study
Bibliographic record
Abstract
Introduction: Poor living conditions and poverty in urban slums mean that informal health providers (IHPs) often dominate health service provision in such settings. We explored the capacity of leaders within slums to contribute to linking IHPs to formal health providers (FHPs), for improved access to quality health services in slums. Method: We purposively selected and interviewed 16 community leaders across 8 urban slums in Enugu and Anambra states in Southeast Nigeria. Transcribed interviews were then analyzed using thematic analysis aided by NVIVO. Finding: Chairpersons and local vigilante security outfits were ubiquitous across urban slum communities- coordinating and influencing actors and health activities within settlements. Oversight functions and lived experiences meant leaders had a good insight into existing community dynamics. Slum leaders acknowledged the differential roles, as well as the strengths and weaknesses of FHPs and IHPs. Linkage establishment was considered potentially useful, and leaders were willing to assist, if the existing shortcomings in FHPs were addressed. Conclusion: Despite being under-recognized, leaders in urban slums have the potential to help the realization of health goals given their grassroots influences. Leaders in urban have strategic positional knowledge and leverage that could catalyze the IHP-FHP linkage conversation and implementation towards improving access to quality healthcare services in slums.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".