Urban primary care networks in Rwanda aim to improve access to healthcare services for urban populations. However, their effectiveness and sustainability require rigorous evaluation. A multilevel regression model was employed to analyse data from clinics within the network. The model accounts for both clinic-level (e. g. , staff training, equipment) and patient-level factors influencing clinical outcomes. The multilevel regression analysis revealed that adequate staffing levels (Clinic Level + imes Patient Level) significantly improved patient satisfaction scores by an average of 15% (95% CI: 8%, 23%) compared to clinics with insufficient staff. The multilevel regression analysis provided insights into the impact of staffing levels on clinical outcomes in urban primary care networks, offering a robust framework for future evaluations and policy development. Clinic managers should prioritise regular training sessions for healthcare providers and ensure sufficient resources to meet patient needs effectively.
Kabirwa Ngandu (Thu,) studied this question.
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