Veille Scientifique étudiante concernant la cognition partagée (SharedCognition) et la polarisation politique
Alimenté par : Claudia Dapino Ponel, Madeline Desmurs
Cette application est une plateforme collaborative de veille scientifique permettant d'importer des publications depuis PubMed, de suivre leur lecture, d'en extraire les éléments méthodologiques clés (protocole, variables, résultats), et de constituer une synthèse structurée pour faciliter la réalisation de revues de littérature.
Dernière synchronisation : 13/09/2026
BMC Health Serv Res . 2026;26 (1)
BACKGROUND: A well-performing health workforce, defined as one that is available, competent, productive, and responsive to patient needs, is central to achieving Sustainable Development Goals SDG 3.8 on 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 defined as enrolled, registered, and degree-level practitioners licensed by the Uganda Nurses and Midwives Council, in Lira District, Northern Uganda.METHODS: A cross-sectional convergent parallel mixed-methods study was conducted from April 2017 to May 2018. A structured questionnaire was administered to 156 randomly selected nurses (n = 98) and midwives (n = 58) across all government (n = 24) and private-not-for-profit (n = 6) 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 (KIIs) and three Focus Group Discussions (n = 30) were analyzed thematically.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. Performance levels varied across dimensions: half of the respondents (50.0%) had competency scores in the 0-50% range, while productivity (60.3%), availability (51.3%), and responsiveness (75.0%) scores were predominantly in the 51-75% range. Linear regression identified distinct predictors for each dimension: Competency was significantly predicted by C1 (Poor Clinical Practice, β=-0.010, p = 0.012), C2 (Adherence to Systems, β = 0.066, p