Impact of Muslim opinion leaders’ training of healthcare providers on the uptake of MNCH services in Northern Nigeria
Bibliographic record
Abstract
Expanding access to maternal, newborn and child health (MNCH) services in traditional societies is a public health challenge, often complicated by cultural and religious beliefs about what is permitted or not permitted within a faith group. This is particularly true in the Muslim majority North of Nigeria, where deep suspicions of Western public health programmes, coupled with failing and underfunded health system, have led to the emergence of a new generation of Muslim Opinion Leaders (MOLs) with counter-narratives against family planning, immunisation and nutrition programmes. This paper reports on an innovative project implemented under the Saving Lives at Birth global partnership programme, where conservative MOLs transformed as champions were engaged as health communicators to train health providers on correct religious precepts related to MNCH. A matched subject type of study design was used to compare healthcare providers' performance in control and intervention health facilities. The result indicates a significant difference both in perception and in practices between healthcare providers in intervention and control facilities, with respect to MNCH uptake. This paper highlights the need for renewed focus on engaging faith leaders and organisations in health communication and service delivery and presents a model of sustainable engagement of champions in MNCH.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".