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Health-Worker Mentorship and Adherence to National Malaria Case-Management Guidelines in Zamfara State, Nigeria: A Comparative Cross-Sectional Study

Domaine:

healthcare

Type de record:

paper
Créateur:
YakHadSanMuk
Éditeur:
Ste
Hôte:
Effective malaria case management depends on diagnostic confirmation, appropriate treatment, accurate documentation, and timely referral. Although health-worker mentorship may improve adherence to malaria guidelines, evidence from security-affected settings remains limited. This study assessed the association between exposure to a three-month health-worker mentorship programme and adherence to national malaria case-management guidelines among primary healthcare workers in Zamfara State, Nigeria. A comparative cross-sectional study was conducted among 360 healthcare workers selected from 60 primary healthcare facilities using multistage sampling. Participants included 180 workers from mentorship-supported local government areas and 180 from non-supported areas. Data were collected using interviewer-administered questionnaires and a 10-item record-review checklist. Group differences were assessed using Pearson chi-square or Fisher’s exact tests, and unadjusted prevalence ratios (PRs) with 95% confidence intervals (CIs) were calculated. Workers in mentorship-supported areas showed higher adherence for documenting fever (100.0% vs 61.7%; PR 1.62, 95% CI 1.45–1.82), conducting malaria rapid diagnostic testing (100.0% vs 41.1%; PR 2.43, 95% CI 2.04–2.90), documenting test results correctly (94.4% vs 28.3%; PR 3.33, 95% CI 2.64–4.22), and providing guideline-concordant treatment (78.9% vs 49.4%; PR 1.60, 95% CI 1.35–1.88). Diagnostic classification did not differ significantly between groups. This study contributes evidence on mentorship-supported malaria case-management implementation in security-affected settings. Exposure to mentorship was associated with improved performance on most adherence indicators; however, the cross-sectional, non-randomised design and group differences limit causal interpretation. Future evaluations should account for confounding, clustering, and commodity availability.

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