Predictors of nurse and midwife performance in a Ugandan district: a mixed-methods study of individual, organizational, and community factors

Abstract

<title>Abstract</title> Background A well-performing health workforce is central to achieving Sustainable Development Goals and Universal Health Coverage. In Uganda, district-level health performance reports have indicated persistent challenges. Addressing health workforce performance gaps is therefore a critical health systems issue for achieving district-level health targets. This study assessed factors affecting the performance of professional nurses and midwives in Lira District, Uganda. Methods A cross-sectional mixed-methods study was conducted from April 2017 to May 2018. A structured questionnaire was administered to 156 randomly selected nurses and midwives across all government and private-not-for-profit facilities. Performance was measured across four dimensions: competency, productivity, availability, and responsiveness. Principal Component Analysis (PCA) reduced independent variables. Linear regression identified predictors of performance. Qualitative data from 20 key informant interviews and three Focus Group Discussions (n = 30) were analyzed thematically. All qualitative findings reported are based exclusively on primary data collected during the study. Results The majority of respondents were female (83.3%), certificate holders (72.4%), and had 1–10 years of experience (54.5%). PCA yielded six components (C1-C6) explaining 85.9% of variance. Key predictors included: adherence to performance standards (C2, β = 0.064, p &lt; 0.001), participation in decision-making (C4), and unfavorable working conditions (C4, β = 0.120, p &lt; 0.001). Linear regression showed components C1, C2, C3, C4 &amp; C5 significantly predicted overall performance (R²=0.882, p &lt; 0.01). Most nurses had competency levels between 0–50%, while productivity, availability, and responsiveness levels were between 51–75%. Qualitative findings highlighted poor health-seeking behaviors (85%), political interference in recruitment/promotion (70%), lack of community ownership of health facilities (80%), and negative staff attitudes (85%) as key contextual factors. Conclusions Individual factors (skills, motivation) and organizational factors (working environment, leadership, resource availability) significantly predict the performance of nurses and midwives. Political interference and poor community engagement further undermine performance. A multi-level intervention addressing individual capacity, organizational support, and community-health system linkages is urgently needed to improve workforce performance and service delivery. This requires coordinated action from national policymakers, district managers, and facility leaders to improve health service delivery.