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

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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 < 0.001), participation in decision-making (C4), and unfavorable working conditions (C4, β = 0.120, p < 0.001). Linear regression showed components C1, C2, C3, C4 & C5 significantly predicted overall performance (R²=0.882, p < 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. Health workforce Performance Nurses Midwives Uganda Health systems Mixed-methods Figures Figure 1 Figure 2 Figure 3 Background The performance of the health workforce is a critical determinant of health system effectiveness and a cornerstone for achieving the Sustainable Development Goals (SDGs) and Universal Health Coverage (UHC) [ 1 , 2 ]. Nurses and midwives constitute over 60% of the health workforce in many countries, including Uganda, playing a pivotal role in primary healthcare delivery, especially in underserved areas [ 3 , 4 ]. Globally, there is a recognized crisis in the health workforce, with a projected shortfall of 18 million health workers by 2030, predominantly in low- and middle-income countries [ 1 , 5 ]. Performance, conceptualized as a combination of availability, competency, productivity, and responsiveness, is influenced by a complex interplay of individual, organizational, and community-level factors (Fig. 1 ) [ 6 , 7 ]. Individual factors such as skills, motivation, job satisfaction, and remuneration are well-documented determinants [ 8 , 9 ]. Organizational factors, including leadership, supervision, resource availability (drugs, equipment), and the working environment, create the context within which care is delivered [ 10 , 11 ]. Furthermore, community perceptions, health-seeking behaviors, and the relationship between communities and health workers significantly impact service utilization and provider morale [ 12 , 13 ]. In Uganda, decentralization was implemented to improve service delivery, but concerns have been raised about its impact on health workforce management and performance [ 14 , 15 ]. Lira District in Northern Uganda has consistently shown poor performance in national health league tables, with deteriorating indicators in immunization, antenatal care, and maternal health outcomes between 2014/15 and 2016/17 [ 16 ]. Concurrent reports highlight high absenteeism, low morale, and poor motivation among health workers in the district [ 17 ]. Despite these challenges, there is limited localized evidence on the specific factors affecting the performance of nurses and midwives, who form the backbone of service delivery. Existing studies in Uganda have often focused on broader health system issues or specific cadres like doctors, leaving a gap in understanding the drivers of performance for this critical group at the district level, which undermines the quality and accessibility of key health services at the district level [ 18 , 19 ]. This study, therefore, sought to determine the individual, organizational, and community factors affecting the performance of professional nurses and midwives in health facilities in Lira District, Uganda. The findings aim to inform targeted interventions and policies to strengthen the nursing and midwifery workforce, ultimately contributing to improved health outcomes. Methods Study design and setting A convergent parallel mixed-methods design was conducted from April 2017 to May 2018 in Lira District, Northern Uganda (Fig. 2 ). Lira District is located in the Lango sub-region (Fig. 3 ) and has one regional referral hospital, 3 Health Centre IVs, 17 Health Centre IIIs, and 10 Health Centre IIs, serving a population of approximately 445,975 [based on 2014 census projections]. Conceptual framework This study was guided by an adapted strategic performance model [ 23 ], which posits that individual attributes and behaviors, shaped by organizational strategy and constraints, determine performance outcomes, with community factors acting as an external influence. The framework (Fig. 1 ) illustrates the relationships between the independent variables (Individual Factors, Organizational Factors, Community Factors) and the dependent variable (Performance of nurses and midwives, measured by Availability, Competency, Productivity, and Responsiveness). This model is appropriate for health services research as it explicitly links individual-level performance to organizational strategy and external contextual factors, providing a systems-level understanding of workforce performance. Study population and sampling The study population comprised all professional nurses and midwives (enrolled, registered, and degree-level) working in Lira District’s public and private-not-for-profit (PNFP) health facilities. Quantitative sample The sample size was calculated using the Kish Leslie formula for a finite population [ 20 ]. With an estimated 354 professional nurses/midwives (76% of the health workforce), a 95% confidence level, a 5% margin of error, and a design effect of 1, a minimum sample of 156 was required. Participants were selected using simple random sampling (lottery method) from a comprehensive facility staff list. Qualitative sample Purposive sampling was used to select 20 key informants (supervisors, in-charges) for in-depth interviews. Three Focus Group Discussions (FGDs), each with 10 participants, were held across the three Health Sub-Districts. FGD participants included Health Unit Management Committee members, local council leaders, and community opinion leaders, excluding current service providers to minimize bias. Data collection Quantitative data were collected using a structured, pre-tested questionnaire adapted from a tool used in a prior Ugandan study on health worker performance [ 21 ]. The tool was further developed for this study and is provided in full in supplementary file 1. The questionnaire had four sections: socio-demographics, individual factors (attributes and behaviors), organizational factors (strategy and constraints), and performance dimensions (dependent variable). Performance was operationalized through 46 items across four sub-scales: Competency (9 items), Productivity (10 items), Availability (17 items), and Responsiveness (10 items). Responses were on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). These items measured self-reported performance in routine service delivery tasks relevant to frontline nursing and midwifery roles (Supplementary file 1). Qualitative data were collected using interview guides for key informants and FGDs. Themes explored included perceptions of performance, influencing factors, and community-health worker interactions (Supplementary file 1). All sessions were audio-recorded, transcribed verbatim, and supplemented with field notes. All qualitative findings presented in this manuscript are derived exclusively from primary data collected during this study. Measurement of variables Dependent variable Performance was a composite average score derived from the four dimensions. Sub-scale scores were also analyzed independently. Independent variables These included socio-demographic variables (age, sex, education, experience, facility level, location) and composite scores for Individual Factors (22 items) and Organizational Factors (23 items) derived from the Likert-scale items. Data quality control The questionnaire was pre-tested, and reliability was assessed using Cronbach’s Alpha. All scales showed good to excellent internal consistency: Individual Factors (α = 0.971), Organizational Factors (α = 0.943), and Performance Dimensions (α = 0.853). Face and content validity were ensured through expert review by supervisors. Data analysis Quantitative data were analyzed using STATA 14. Descriptive statistics summarized background characteristics. Principal Component Analysis (PCA) with varimax rotation was used for data reduction on the 45 items from individual and organizational factors. Components with eigenvalues > 1 were retained (Kaiser’s criterion) [ 22 ]. Pearson correlation assessed relationships between components and performance dimensions. Multiple linear regression models identified predictors of each performance dimension and overall performance, with statistical significance set at p < 0.05. Qualitative data were analyzed using thematic content analysis. Transcripts were coded manually, and categories were grouped into themes and sub-themes through an iterative process. Only data collected through this study's interviews and FGDs are reported in the results section. Ethical considerations Ethical approval was obtained from the Makerere University School of Public Health Research and Ethics Committee and the Uganda National Council for Science and Technology (Ref: SS 1234). Administrative clearance was granted by Lira District Health Office. Written informed consent was obtained from all participants. Confidentiality was maintained through anonymization of data. Results Socio-demographic characteristics of respondents A total of 156 nurses and midwives participated (response rate ~ 100%). As shown in Table 1 , the majority were female (83.3%), aged 18–30 years (39.1%), and married (73.7%). Most (72.4%) held certificates (enrolled nurse/midwife), and 54.5% had 1–10 years of work experience. A plurality (40.4%) worked at Health Centre IIIs, and 78.9% were based in rural facilities. Table 1 Background characteristics of respondents (n = 156) Characteristic Frequency (n) Percentage (%) Age group 18–30 years 61 39.1 31–40 years 49 31.4 41–50 years 39 25.0 51–60 years 7 4.5 Highest qualification Bachelor's Degree 3 1.9 Diploma 40 25.6 Certificate 113 72.4 Sex Male 26 16.7 Female 130 83.3 Years of experience 1–10 years 85 54.5 11–20 years 61 39.1 21–30 years 8 5.1 > 31 years 2 1.3 Facility level Hospital 19 12.2 Health Centre IV 43 27.6 Health Centre III 63 40.4 Health Centre II 31 19.9 Facility location Urban 33 21.2 Rural 123 78.8 Principal Component Analysis (PCA) PCA reduced the 45 independent variable items to six components (C1-C6) with eigenvalues > 1, accounting for 85.9% of the total variance (Table 2 ). The rotated component matrix (Table 3 ) revealed the following key latent variables: C1 "Poor Clinical Practice" (e.g., not consulting treatment guidelines). C2 "Adherence to Systems & External Influence" (e.g., following standards, political interference in promotion). C3 "Organizational Commitment & Resource Lack" (e.g., recommending the organization, lack of supplies, low effort). C4 "Participatory Environment & Working Conditions" (e.g., decision-making, poor work environment, data use). C5 "Unmet Training & Remuneration Needs" (e.g., training not based on appraisal, low pay). C6 "Skills & Politicized Recruitment" (e.g., having required skills, political recruitment influence). Table 2 Eigenvalues and variance explained by principal components Component Eigenvalue Difference Proportion Cumulative 1 21.97 15.56 0.488 0.488 2 6.41 1.72 0.142 0.631 3 4.69 2.04 0.104 0.735 4 2.65 0.80 0.059 0.794 5 1.85 0.73 0.041 0.835 6 1.12 - 0.025 0.859 The key components are summarized in Table 3 ; the full rotated component matrix is available in Supplementary file 2: Table S1 . Table 3 Rotated component matrix of factors affecting performance (key loadings ≥ |0.28|) Component Interpretive Label Key Variables with High Loadings Direction C1 Poor Clinical Practice Not consulting treatment guidelines Negative C2 Adherence to Systems & External Influence Adhere to performance standards; Political interference in promotion Mixed C3 Organizational Commitment & Resource Lack Recommend organization; Lack of supplies; Low effort Mixed C4 Participatory Environment & Working Conditions Participate in decision-making; Unfavorable work environment Positive/Negative C5 Unmet Training & Remuneration Needs Training not based on appraisal; Low pay Negative C6 Skills & Politicized Recruitment Have required skills; Political influence in recruitment Mixed Note: Full rotated component matrix is available in Supplementary file 2: Table S1 . Performance levels The average scores for performance dimensions were: Competency (3.03 ± 0.44), Productivity (3.01 ± 0.47), Availability (2.61 ± 0.68), and Responsiveness (2.97 ± 0.60). Categorization into percentile levels (Table 4 ) revealed that half (50.0%) of the respondents had competency levels in the 0–50% range. Productivity (60.3%), Availability (51.3%), and Responsiveness (75.0%) levels were predominantly in the 51–75% range. Table 4 Performance level categorization by dimension Performance Dimension 0–50% 51–75% 76–100% Competency 50.0% 48.1% 1.9% Productivity 26.9% 60.3% 12.8% Availability 45.5% 51.3% 3.2% Responsiveness 20.5% 75.0% 4.5% Predictors of performance: Linear regression analysis Multiple linear regression models were run with each performance dimension and the overall performance score as dependent variables, and the six PCA components as independent variables. The consolidated results are presented in Supplementary file 3: Table S2 . Key findings are summarized below: Overall performance (R² = 0.882): Components C1 (β= -0.011, p = 0.006), C2 (β = 0.064, p < 0.001), C3 (β = 0.020, p = 0.023), C4 (β = 0.120, p < 0.001), and C5 (β= -0.036, p = 0.001) were significant predictors. Competency (R² = 0.835): Significantly predicted by C1 (β= -0.010, p = 0.012), C2 (β = 0.066, p < 0.001), C3 (β = 0.039, p < 0.001), and C4 (β = 0.080, p < 0.001). Productivity (R² = 0.559): Significantly predicted by C1 (β= -0.029, p < 0.001), C2 (β = 0.030, p = 0.006), C3 (β = 0.044, p = 0.005), and C4 (β = 0.131, p < 0.001). Availability (R² = 0.902): Significantly predicted by C2 (β = 0.090, p < 0.001), C4 (β = 0.131, p < 0.001), C5 (β= -0.060, p < 0.001), and C6 (β= -0.020, p = 0.004). Responsiveness (R² = 0.951): Significantly predicted by C2 (β = 0.070, p < 0.001), C4 (β = 0.139, p < 0.001), and C5 (β= -0.047, p < 0.001). Qualitative findings Thematic analysis converged with and enriched the quantitative findings, identifying three overarching themes: All quotations presented are verbatim from the study participants collected during the research period. Organizational factors constraining performance: Sub-theme: Resource inadequacy : Chronic stock-outs of medicines and supplies, lack of basic equipment, and inadequate infrastructure were frequently cited as major demotivators and barriers to providing quality care. Participant quote “The work environment is a big problem here, the workspace is inadequate with many cracks on the walls and floors, patients sleep on floors, lack of ventilation, excessive noise, inappropriate lighting, poor social services, lack of personal protective equipment , and this is really frustrating for us.” (FGD participant) Participant quote “In most of the health facilities in the district, there is lack of clean and safe water supply as a major setback to the quality of care delivered by professional nurses and midwives.” (FGD participant) Participant quote “Due to inadequate supply of medicines and supplies, lack of medical equipment and poor working conditions across all health facilities, it is difficult to assess the competencies of professional nurses and midwives. However, some of them seem to be competent. ” (Key informant) Participant quote ‟Stock outs of drugs are a common occurrence here, and sometimes the community accuses health workers of stealing drugs when actually the amount of drugs supplied cannot even last for a month.” (FGD participant) Sub-theme: Poor leadership & work environment : Participants reported unsupportive supervision, poor communication, and a lack of participatory decision-making. Heavy workload due to rigid staffing norms was a severe stressor. Participant quote ‟Here, there is poor network, and when you want to communicate you have to search for a proper network that can be stable and also there are lots of conflicts among nurse’s due to poor communication, mostly during the handover of duty.” (FGD participant) Participant quote “The manager does not have enough authority to change these policies and the structure; therefore, if you cannot change the policies and the structure, you cannot execute a decision easily, and this then affects performance negatively.” (FGD participant) Participant quote “In my health center the staffing level is about 60% but the services are overwhelming, due to rigid staffing norm that was developed in 2002, not considering the increase in population, new emerging conditions, and new standard policies developed, the available number of health workers are inadequate to provide quality, efficient and effective health service deliveries, professional nurse and midwives experience increased workloads, burnt out and low morale as one midwifes has to handle all clients for day and one for night without night off duties resulting to high staff turnover for better opportunities elsewhere.” (Key informant) Individual and political economy factors: Sub-theme: Low morale and motivation : Poor remuneration, lack of recognition, and bias in promotions and training opportunities were highlighted as key demotivators. Participant quote “ Professional nurses and midwives are stressed and burnt out due to heavy workload, poor remuneration and low motivation that will affect their customer care attitude. ” (Key informant) Participant quote: “ I don’t really understand why we health workers are the least paid civil servant. Members of parliament are getting more and more allowances...” (FGD participant) Participant quote “Health workers in Uganda are the least paid in the whole east African region, that’s why you see our doctors and nurses moving to other countries where pay is good...” (FGD participant) Sub-theme: Political interference : A dominant concern was the influence of local politicians in the recruitment, deployment, and promotion of health staff, which was perceived to undermine meritocracy and discipline. Participant quote “There is also political influence and interference. Some of these people who are recruited may be related to some politicians, and they want them to be working in certain areas, so it makes distribution of staff difficult.” (Key informant) Community-Health worker dynamics: Sub-theme: Poor health-seeking behaviors : Communities were reported to have poor health-seeking behaviors, often preferring traditional medicine or faith healers, and presenting late to facilities, which frustrated health workers. Participant quote “I observed that because communities are very poor, sometimes people decide to come to the health facility when they are too late for health workers to save their lives. This reflects badly on the health workers because when patients die, they feel that they could have done something to save lives.” (Key informant) Participant quote “For us when you fall sick, you go to the drugs shops and explain your pains to the attendant, who chooses the drugs, in case things do not work out, you go to the private clinic, there the clinic nurse is more technical than the drug shops attendant who once defeated may refer you to a health center where possibly when your condition worsens, they refer you to hospital.” (FGD participant) Participant quote “Our community still strongly prefers traditional medicines, which result in poor seeking behaviors.” (FGD participant) Participant quote “Our community of recent has recently turned to prefer “balokole” (born-again Christians) just to help them pray for conditions like fever, snake bite, diarrhea, serious fall from a tree, because of the perceptions or lack of trust in the usefulness of some medical interventions.” (FGD participant) Sub-theme: Negative mutual perceptions : Health workers were criticized for having negative attitudes and providing poor customer care. Conversely, communities were perceived as lacking appreciation and a sense of ownership over local health facilities. Participant quote “The professional nurses and midwives treat us badly, like we are not human beings. They may pay no attention if someone dies compared with the traditional birth attendants who treat people humanely. In health centres/hospitals, they can slap you and say, ʽTo avoid disturbances, let’s do the caesarean operationʼ. This brings fear and scepticism in using the health services.” (FGD participant) Participant quote “If you have grown up in poverty, you may look older than you actually are, and they will abuse you and say, 'Look at that old woman who has come to give birth.” (FGD participant) Discussion This mixed-methods study provides a comprehensive analysis of the multi-level factors affecting nurse and midwife performance in a Ugandan district. The quantitative findings demonstrate that both individual attributes (e.g., clinical practice, commitment) and organizational factors (e.g., work environment, systems adherence) are strong, statistically significant predictors of all performance dimensions, explaining a large proportion of the variance (R² up to 0.951). This aligns with the strategic performance model by Noe et al. [ 23 ], which posits that individual characteristics and behaviors, shaped by the organizational context, are vital for performance. The low competency scores (50% in the bottom half) are particularly concerning and are linked to poor clinical practices like not consulting treatment guidelines (C1). This suggests a gap between knowledge and practice, possibly due to inadequate supervision, lack of access to guidelines, or high workload pressures. Similar findings have been reported in other low-resource settings, where clinical decision-making often deviates from standards due to systemic constraints [ 24 , 25 ]. This finding resonate with our qualitative data where participants noted challenges in accessing and applying clinical guidelines consistently. These competency gaps likely translate to substandard clinical care and poor patient outcomes, directly affecting the quality of health services delivered. The strong predictive power of components related to the work environment and participation (C4) underscores the critical role of organizational health. Unsafe, under-resourced, and non-participatory workplaces are known to lead to burnout, low morale, and high turnover [ 26 , 27 ]. Our qualitative data vividly describe dilapidated infrastructure, stock-outs, and lack of supportive supervision, creating a context where even motivated workers struggle to perform optimally. This resonates with studies across Sub-Saharan Africa highlighting the demoralizing effect of chronic resource shortages on health workers [ 28 , 29 ]. The prominence of political interference in recruitment and promotion (C2, C6) as a demotivating factor is a critical finding. Decentralization in Uganda aimed to improve local accountability but appears to have facilitated political capture of human resource processes, undermining merit-based systems and professional autonomy [ 14 , 30 ]. This fosters a climate of unfairness, reduces job security, and can lead to the placement of underqualified personnel, directly impacting service quality [ 31 ]. Our qualitative findings directly corroborate this, with participants expressing frustration over political influences in staffing decisions. The community-level factors identified-poor health-seeking behaviors and negative attitudes-highlight a reciprocal breakdown in the patient-provider relationship. Communities’ late presentation and preference for alternative care increase clinical severity and workload, while perceived provider rudeness decreases trust and utilization, creating a vicious cycle [ 12 , 32 ]. This lack of social capital and community ownership is a significant barrier to building resilient and responsive health systems [ 33 ]. Our study's qualitative findings provide first-hand accounts of this dynamic from both health worker and community perspectives. Limitation of the study Our study has limitations. Its cross-sectional design limits causal inference. The performance measure was self-reported, which may be subject to social desirability bias. However, the use of a validated tool, high reliability scores, and triangulation with qualitative data strengthen validity. The focus on one district may affect generalizability, though the findings likely reflect challenges common to many rural, decentralized districts in Uganda and similar contexts. Implications of the study This study has important implications for health services management and policy. For district health managers, our findings highlight actionable areas: insulating recruitment from politics, instituting regular supportive supervision, and creating feedback mechanisms with communities. At the national level, policy revisions are needed to update rigid staffing norms and ensure predictable funding for essential medicines and infrastructure, which are foundational to health worker performance and, ultimately, service quality. This study not only identifies key predictors of nurse and midwife performance but also carries important implications for future research. Theoretically, it reinforces the need to adapt existing performance models to account for political and community-level factors in decentralized health systems. Methodologically, it demonstrates the utility of mixed-methods approaches and PCA for distilling complex constructs in health workforce studies. Future studies should employ longitudinal designs to establish causal pathways and evaluate the impact of multi-level interventions on sustained performance improvement. Conclusions The performance of nurses and midwives, the frontline of health service delivery in Lira District, is undermined by a confluence of factors across the health system ecosystem. Key issues include poor clinical practices, demotivating work environments, critical resource shortages, politicization of human resource management, and fractured community-health system relationships. To improve performance, we recommend: Strengthen human resource systems Centralize and depoliticize recruitment and promotion processes to ensure meritocracy (e.g., through independent district recruitment panels). Implement transparent, performance-based career development and incentive structures (linked to service delivery metrics). Invest in the work environment Prioritize targeted investments to ensure consistent availability of essential medicines, equipment, and functional infrastructure (including water and power), with district health offices tracking stock-out rates. Develop and disseminate standard operating procedures for all service points, with regular compliance audits. Enhance supervision and support Reinvigorate supportive supervision and mentorship focused on clinical competency and adherence to guidelines. Foster participatory leadership and management at facility levels. Engage communities Implement structured community engagement programs to improve health-seeking behaviors, rebuild trust, and foster a sense of co-ownership of local health services. Policy revision Review the decentralized human resource management framework to curb political interference while maintaining local accountability. Addressing these multi-faceted challenges requires a coordinated, system-strengthening approach from national policymakers, district managers, facility leaders, and communities to empower and enable nurses and midwives to perform to their full potential. Abbreviations DHIS2 District Health Information System FGD Focus Group Discussion MOH Ministry of Health PCA Principal Component Analysis PNFP Private Not-For-Profit SDG Sustainable Development Goals UHC Universal Health Coverage UNMHCP Uganda National Minimum Health Care Package WHO World Health Organization Declarations Ethics approval and consent to participate Ethical approval was granted by the Makerere University School of Public Health Research and Ethics Committee and the Uganda National Council for Science and Technology (Ref: SS 1234). Written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was conducted as part of an academic degree (MPH). No external funding was received. Authors' contributions EA conceptualized the study, developed methodology, conducted investigation, performed formal analysis, and wrote the original draft. JPB contributed to analysis, writing, review & editing. RK and ER supervised the study, provided resources, and contributed to review & editing. All authors read and approved the final manuscript. Acknowledgements The authors thank the Ministry of Health Uganda, Makerere University, Lira District Health Office, all health facility in-charges, and the nurses, midwives, and community members who participated in this study. 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Prytherch H, Kagone M, Aninanya GA, Williams JE, Kakoko DC, Leshabari MT, et al. Motivation and incentives of rural maternal and neonatal health care providers: a comparison of qualitative findings from Burkina Faso, Ghana and Tanzania. BMC Health Serv Res. 2013;13:149. Witter S, Fretheim A, Kessy FL, Lindahl AK. Paying for performance to improve the delivery of health interventions in low- and middle-income countries. Cochrane Database Syst Rev. 2012;2:CD007899. Hongoro C, McPake B. How to bridge the gap in human resources for health. Lancet. 2004;364(9443):1451–6. Gilson L. Trust and the development of health care as a social institution. Soc Sci Med. 2003;56(7):1453–68. Rifkin SB. Lessons from community participation in health programmes: a review of the post Alma-Ata experience. Int Health. 2009;1(1):31–6. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1Questionnaireformusedinthestudy.pdf Supplementaryfile2TableS1FullrotatedcomponentmatrixfromPCA.docx Supplementaryfile3TableS2consolidatedlinearregressionresults.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 26 Jan, 2026 Editor assigned by journal 26 Jan, 2026 Editor invited by journal 20 Jan, 2026 Submission checks completed at journal 20 Jan, 2026 First submitted to journal 20 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8513826","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581783154,"identity":"437b14c8-e43f-453b-b08d-dc9fccc2ac5e","order_by":0,"name":"Edmonton Acheka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACCQZmECnHD+IkFBCvxcZYsgGkxYB4LWmJBgdAPGK08M9uPmzw48/hBOPzqxM/PDBgkOcXO0DAkjvHkhN72w7nmd14u1kC6DDDmbMT8GsxkMgxPsDbcLjY7MbZDSAtCQa3CWrJ/3zwz5/DiZtnnN38g0gtOczJPGxpiRv4e7cRZ4vEjTRjY9k2G2OJG7zbLBIMJAj7hX9G8mPJN3+AUdl/dvPNHxU28vzSBLQg2QdWKUGscrB9B0hRPQpGwSgYBSMJAABWqkV2NQoIgAAAAABJRU5ErkJggg==","orcid":"","institution":"Lira District Local Government","correspondingAuthor":true,"prefix":"","firstName":"Edmonton","middleName":"","lastName":"Acheka","suffix":""},{"id":581783155,"identity":"5b7ec557-d262-4fb3-94b6-6cd7da9a16e8","order_by":1,"name":"John Paul Byagamy","email":"","orcid":"","institution":"Lira District Local Government","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Paul","lastName":"Byagamy","suffix":""},{"id":581783156,"identity":"eb3e70ce-d05c-4a71-95da-6606a659222a","order_by":2,"name":"Richard Kajura","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Kajura","suffix":""},{"id":581783157,"identity":"808da7ea-8482-4e77-bfd5-86885560b5d2","order_by":3,"name":"Elizeus Rutebemberwa","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Elizeus","middleName":"","lastName":"Rutebemberwa","suffix":""}],"badges":[],"createdAt":"2026-01-04 14:38:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8513826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8513826/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101752317,"identity":"e8a6a1dd-14d1-4809-85fc-be783c11dee1","added_by":"auto","created_at":"2026-02-03 10:26:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219194,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework of factors affecting the performance of professional nurses and midwives in Lira District, Uganda (Adapted from Noe et al., 2008 [23]).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/1c4964d90beaadf45e4b4259.jpg"},{"id":101450083,"identity":"8dd9c78f-ea4c-4284-90e1-17446486ba33","added_by":"auto","created_at":"2026-01-29 20:06:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180557,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Uganda indicating the location of Lira District (shaded in red).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/976b5d8a13fb0dcaf60bcf3a.jpg"},{"id":101450085,"identity":"759cd92c-a3fd-49b2-b846-f28876f29c53","added_by":"auto","created_at":"2026-01-29 20:06:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109215,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Lira District showing its administrative divisions and the distribution of health facilities.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/562235215598a6a3ef1db299.jpg"},{"id":102397187,"identity":"3ea30720-6875-4d8b-8afa-87825274f539","added_by":"auto","created_at":"2026-02-11 10:08:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1888421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/475342d5-0024-4fc2-9ca0-9f4718d5f5ed.pdf"},{"id":101450087,"identity":"a37a3c3c-18b6-484e-9709-ea2fcdc9ce66","added_by":"auto","created_at":"2026-01-29 20:06:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":317684,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1Questionnaireformusedinthestudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/a9ff78f98480e43b3b0ee038.pdf"},{"id":101751727,"identity":"79ccbedd-9c02-4318-a2e7-1092dcdf3a0b","added_by":"auto","created_at":"2026-02-03 10:22:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16292,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2TableS1FullrotatedcomponentmatrixfromPCA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/86d2463d2c1ac91aaa3ef81f.docx"},{"id":101450086,"identity":"fc48c5d9-849f-4af8-84ec-0035d5e99803","added_by":"auto","created_at":"2026-01-29 20:06:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21897,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3TableS2consolidatedlinearregressionresults.docx","url":"https://assets-eu.researchsquare.com/files/rs-8513826/v1/7afccdc02e04df91de55894f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of nurse and midwife performance in a Ugandan district: a mixed-methods study of individual, organizational, and community factors","fulltext":[{"header":"Background","content":"\u003cp\u003eThe performance of the health workforce is a critical determinant of health system effectiveness and a cornerstone for achieving the Sustainable Development Goals (SDGs) and Universal Health Coverage (UHC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nurses and midwives constitute over 60% of the health workforce in many countries, including Uganda, playing a pivotal role in primary healthcare delivery, especially in underserved areas [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Globally, there is a recognized crisis in the health workforce, with a projected shortfall of 18\u0026nbsp;million health workers by 2030, predominantly in low- and middle-income countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerformance, conceptualized as a combination of availability, competency, productivity, and responsiveness, is influenced by a complex interplay of individual, organizational, and community-level factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Individual factors such as skills, motivation, job satisfaction, and remuneration are well-documented determinants [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Organizational factors, including leadership, supervision, resource availability (drugs, equipment), and the working environment, create the context within which care is delivered [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, community perceptions, health-seeking behaviors, and the relationship between communities and health workers significantly impact service utilization and provider morale [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Uganda, decentralization was implemented to improve service delivery, but concerns have been raised about its impact on health workforce management and performance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lira District in Northern Uganda has consistently shown poor performance in national health league tables, with deteriorating indicators in immunization, antenatal care, and maternal health outcomes between 2014/15 and 2016/17 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Concurrent reports highlight high absenteeism, low morale, and poor motivation among health workers in the district [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these challenges, there is limited localized evidence on the specific factors affecting the performance of nurses and midwives, who form the backbone of service delivery. Existing studies in Uganda have often focused on broader health system issues or specific cadres like doctors, leaving a gap in understanding the drivers of performance for this critical group at the district level, which undermines the quality and accessibility of key health services at the district level [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study, therefore, sought to determine the individual, organizational, and community factors affecting the performance of professional nurses and midwives in health facilities in Lira District, Uganda. The findings aim to inform targeted interventions and policies to strengthen the nursing and midwifery workforce, ultimately contributing to improved health outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA convergent parallel mixed-methods design was conducted from April 2017 to May 2018 in Lira District, Northern Uganda (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Lira District is located in the Lango sub-region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and has one regional referral hospital, 3 Health Centre IVs, 17 Health Centre IIIs, and 10 Health Centre IIs, serving a population of approximately 445,975 [based on 2014 census projections].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConceptual framework\u003c/h3\u003e\n\u003cp\u003eThis study was guided by an adapted strategic performance model [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which posits that individual attributes and behaviors, shaped by organizational strategy and constraints, determine performance outcomes, with community factors acting as an external influence. The framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the relationships between the independent variables (Individual Factors, Organizational Factors, Community Factors) and the dependent variable (Performance of nurses and midwives, measured by Availability, Competency, Productivity, and Responsiveness). This model is appropriate for health services research as it explicitly links individual-level performance to organizational strategy and external contextual factors, providing a systems-level understanding of workforce performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStudy population and sampling\u003c/h3\u003e\n\u003cp\u003eThe study population comprised all professional nurses and midwives (enrolled, registered, and degree-level) working in Lira District\u0026rsquo;s public and private-not-for-profit (PNFP) health facilities.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eQuantitative sample\u003c/strong\u003e \u003cp\u003eThe sample size was calculated using the Kish Leslie formula for a finite population [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. With an estimated 354 professional nurses/midwives (76% of the health workforce), a 95% confidence level, a 5% margin of error, and a design effect of 1, a minimum sample of 156 was required. Participants were selected using simple random sampling (lottery method) from a comprehensive facility staff list.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eQualitative sample\u003c/strong\u003e \u003cp\u003ePurposive sampling was used to select 20 key informants (supervisors, in-charges) for in-depth interviews. Three Focus Group Discussions (FGDs), each with 10 participants, were held across the three Health Sub-Districts. FGD participants included Health Unit Management Committee members, local council leaders, and community opinion leaders, excluding current service providers to minimize bias.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eQuantitative data\u003c/b\u003e were collected using a structured, pre-tested questionnaire adapted from a tool used in a prior Ugandan study on health worker performance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The tool was further developed for this study and is provided in full in supplementary file 1. The questionnaire had four sections: socio-demographics, individual factors (attributes and behaviors), organizational factors (strategy and constraints), and performance dimensions (dependent variable). Performance was operationalized through 46 items across four sub-scales: Competency (9 items), Productivity (10 items), Availability (17 items), and Responsiveness (10 items). Responses were on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 5\u0026thinsp;=\u0026thinsp;Strongly Agree). These items measured self-reported performance in routine service delivery tasks relevant to frontline nursing and midwifery roles (Supplementary file 1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQualitative data\u003c/b\u003e were collected using interview guides for key informants and FGDs. Themes explored included perceptions of performance, influencing factors, and community-health worker interactions (Supplementary file 1). All sessions were audio-recorded, transcribed verbatim, and supplemented with field notes. All qualitative findings presented in this manuscript are derived exclusively from primary data collected during this study.\u003c/p\u003e\n\u003ch3\u003eMeasurement of variables\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eDependent variable\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePerformance\u003c/em\u003e was a composite average score derived from the four dimensions. Sub-scale scores were also analyzed independently.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIndependent variables\u003c/strong\u003e \u003cp\u003eThese included socio-demographic variables (age, sex, education, experience, facility level, location) and composite scores for \u003cem\u003eIndividual Factors\u003c/em\u003e (22 items) and \u003cem\u003eOrganizational Factors\u003c/em\u003e (23 items) derived from the Likert-scale items.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData quality control\u003c/h2\u003e \u003cp\u003eThe questionnaire was pre-tested, and reliability was assessed using Cronbach\u0026rsquo;s Alpha. All scales showed good to excellent internal consistency: Individual Factors (α\u0026thinsp;=\u0026thinsp;0.971), Organizational Factors (α\u0026thinsp;=\u0026thinsp;0.943), and Performance Dimensions (α\u0026thinsp;=\u0026thinsp;0.853). Face and content validity were ensured through expert review by supervisors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eQuantitative data\u003c/b\u003e were analyzed using STATA 14. Descriptive statistics summarized background characteristics. Principal Component Analysis (PCA) with varimax rotation was used for data reduction on the 45 items from individual and organizational factors. Components with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 were retained (Kaiser\u0026rsquo;s criterion) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Pearson correlation assessed relationships between components and performance dimensions. Multiple linear regression models identified predictors of each performance dimension and overall performance, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQualitative data\u003c/b\u003e were analyzed using thematic content analysis. Transcripts were coded manually, and categories were grouped into themes and sub-themes through an iterative process. Only data collected through this study's interviews and FGDs are reported in the results section.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Makerere University School of Public Health Research and Ethics Committee and the Uganda National Council for Science and Technology (Ref: SS 1234). Administrative clearance was granted by Lira District Health Office. Written informed consent was obtained from all participants. Confidentiality was maintained through anonymization of data.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics of respondents\u003c/h2\u003e \u003cp\u003eA total of 156 nurses and midwives participated (response rate\u0026thinsp;~\u0026thinsp;100%). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the majority were female (83.3%), aged 18\u0026ndash;30 years (39.1%), and married (73.7%). Most (72.4%) held certificates (enrolled nurse/midwife), and 54.5% had 1\u0026ndash;10 years of work experience. A plurality (40.4%) worked at Health Centre IIIs, and 78.9% were based in rural facilities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBackground characteristics of respondents (n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest qualification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCertificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;31 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacility level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Centre IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Centre III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Centre II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacility location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003ePCA reduced the 45 independent variable items to six components (C1-C6) with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, accounting for 85.9% of the total variance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The rotated component matrix (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed the following key latent variables:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC1\u003c/strong\u003e \u003cp\u003e \"Poor Clinical Practice\" (e.g., not consulting treatment guidelines).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC2\u003c/strong\u003e \u003cp\u003e\"Adherence to Systems \u0026amp; External Influence\" (e.g., following standards, political interference in promotion).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC3\u003c/strong\u003e \u003cp\u003e\"Organizational Commitment \u0026amp; Resource Lack\" (e.g., recommending the organization, lack of supplies, low effort).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC4\u003c/strong\u003e \u003cp\u003e\"Participatory Environment \u0026amp; Working Conditions\" (e.g., decision-making, poor work environment, data use).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC5\u003c/strong\u003e \u003cp\u003e\"Unmet Training \u0026amp; Remuneration Needs\" (e.g., training not based on appraisal, low pay).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eC6\u003c/strong\u003e \u003cp\u003e\"Skills \u0026amp; Politicized Recruitment\" (e.g., having required skills, political recruitment influence).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvalues and variance explained by principal components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe key components are summarized in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cem\u003ethe full rotated component matrix is available in Supplementary file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRotated component matrix of factors affecting performance (key loadings \u0026ge; |0.28|)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterpretive Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Variables with High Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor Clinical Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot consulting treatment guidelines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdherence to Systems \u0026amp; External Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdhere to performance standards; Political interference in promotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganizational Commitment \u0026amp; Resource Lack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecommend organization; Lack of supplies; Low effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipatory Environment \u0026amp; Working Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipate in decision-making; Unfavorable work environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive/Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmet Training \u0026amp; Remuneration Needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining not based on appraisal; Low pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkills \u0026amp; Politicized Recruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHave required skills; Political influence in recruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e \u003cem\u003eNote: Full rotated component matrix is available in Supplementary file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/em\u003e \u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePerformance levels\u003c/h2\u003e \u003cp\u003eThe average scores for performance dimensions were: Competency (3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44), Productivity (3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47), Availability (2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68), and Responsiveness (2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60). Categorization into percentile levels (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed that half (50.0%) of the respondents had competency levels in the 0\u0026ndash;50% range. Productivity (60.3%), Availability (51.3%), and Responsiveness (75.0%) levels were predominantly in the 51\u0026ndash;75% range.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance level categorization by dimension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u0026ndash;75%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u0026ndash;100%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompetency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of performance: Linear regression analysis\u003c/h2\u003e \u003cp\u003eMultiple linear regression models were run with each performance dimension and the overall performance score as dependent variables, and the six PCA components as independent variables. The consolidated results are presented in Supplementary file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Key findings are summarized below:\u003c/p\u003e \u003cp\u003e \u003cb\u003eOverall performance\u003c/b\u003e (R\u0026sup2; = 0.882): Components C1 (β= -0.011, p\u0026thinsp;=\u0026thinsp;0.006), C2 (β\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C3 (β\u0026thinsp;=\u0026thinsp;0.020, p\u0026thinsp;=\u0026thinsp;0.023), C4 (β\u0026thinsp;=\u0026thinsp;0.120, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and C5 (β= -0.036, p\u0026thinsp;=\u0026thinsp;0.001) were significant predictors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCompetency\u003c/b\u003e (R\u0026sup2; = 0.835): Significantly predicted by C1 (β= -0.010, p\u0026thinsp;=\u0026thinsp;0.012), C2 (β\u0026thinsp;=\u0026thinsp;0.066, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C3 (β\u0026thinsp;=\u0026thinsp;0.039, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and C4 (β\u0026thinsp;=\u0026thinsp;0.080, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eProductivity\u003c/b\u003e (R\u0026sup2; = 0.559): Significantly predicted by C1 (β= -0.029, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C2 (β\u0026thinsp;=\u0026thinsp;0.030, p\u0026thinsp;=\u0026thinsp;0.006), C3 (β\u0026thinsp;=\u0026thinsp;0.044, p\u0026thinsp;=\u0026thinsp;0.005), and C4 (β\u0026thinsp;=\u0026thinsp;0.131, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAvailability\u003c/b\u003e (R\u0026sup2; = 0.902): Significantly predicted by C2 (β\u0026thinsp;=\u0026thinsp;0.090, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C4 (β\u0026thinsp;=\u0026thinsp;0.131, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C5 (β= -0.060, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and C6 (β= -0.020, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003cb\u003eResponsiveness\u003c/b\u003e (R\u0026sup2; = 0.951): Significantly predicted by C2 (β\u0026thinsp;=\u0026thinsp;0.070, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C4 (β\u0026thinsp;=\u0026thinsp;0.139, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and C5 (β= -0.047, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQualitative findings\u003c/h2\u003e \u003cp\u003eThematic analysis converged with and enriched the quantitative findings, identifying three overarching themes: All quotations presented are verbatim from the study participants collected during the research period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eOrganizational factors constraining performance:\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSub-theme: Resource inadequacy\u003c/b\u003e: Chronic stock-outs of medicines and supplies, lack of basic equipment, and inadequate infrastructure were frequently cited as major demotivators and barriers to providing quality care.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The work environment is a big problem here, the workspace is inadequate with many cracks on the walls and floors, patients sleep on floors, lack of ventilation, excessive noise, inappropriate lighting, poor social services, lack of personal protective equipment\u003c/em\u003e, \u003cem\u003eand this is really frustrating for us.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;In most of the health facilities in the district, there is lack of clean and safe water supply as a major setback to the quality of care delivered by professional nurses and midwives.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Due to inadequate supply of medicines and supplies, lack of medical equipment and poor working conditions across all health facilities, it is difficult to assess the competencies of professional nurses and midwives. However, some of them seem to be competent.\u003c/em\u003e\u0026rdquo; (Key informant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e‟Stock outs of drugs are a common occurrence here, and sometimes the community accuses health workers of stealing drugs when actually the amount of drugs supplied cannot even last for a month.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSub-theme: Poor leadership \u0026amp; work environment\u003c/b\u003e: Participants reported unsupportive supervision, poor communication, and a lack of participatory decision-making. Heavy workload due to rigid staffing norms was a severe stressor.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e‟Here, there is poor network, and when you want to communicate you have to search for a proper network that can be stable and also there are lots of conflicts among nurse\u0026rsquo;s due to poor communication, mostly during the handover of duty.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The manager does not have enough authority to change these policies and the structure; therefore, if you cannot change the policies and the structure, you cannot execute a decision easily, and this then affects performance negatively.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;In my health center the staffing level is about 60% but the services are overwhelming, due to rigid staffing norm that was developed in 2002, not considering the increase in population, new emerging conditions, and new standard policies developed, the available number of health workers are inadequate to provide quality, efficient and effective health service deliveries, professional nurse and midwives experience increased workloads, burnt out and low morale as one midwifes has to handle all clients for day and one for night without night off duties resulting to high staff turnover for better opportunities elsewhere.\u0026rdquo;\u003c/em\u003e(Key informant)\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIndividual and political economy factors:\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSub-theme: Low morale and motivation\u003c/b\u003e: Poor remuneration, lack of recognition, and bias in promotions and training opportunities were highlighted as key demotivators.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eProfessional nurses and midwives are stressed and burnt out due to heavy workload, poor remuneration and low motivation that will affect their customer care attitude.\u003c/em\u003e\u0026rdquo; (Key informant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipant quote: \u0026ldquo;\u003c/b\u003e \u003cem\u003eI don\u0026rsquo;t really understand why we health workers are the least paid civil servant. Members of parliament are getting more and more allowances...\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Health workers in Uganda are the least paid in the whole east African region, that\u0026rsquo;s why you see our doctors and nurses moving to other countries where pay is good...\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSub-theme: Political interference\u003c/b\u003e: A dominant concern was the influence of local politicians in the recruitment, deployment, and promotion of health staff, which was perceived to undermine meritocracy and discipline.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;There is also political influence and interference. Some of these people who are recruited may be related to some politicians, and they want them to be working in certain areas, so it makes distribution of staff difficult.\u0026rdquo;\u003c/em\u003e (Key informant)\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCommunity-Health worker dynamics:\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSub-theme: Poor health-seeking behaviors\u003c/b\u003e: Communities were reported to have poor health-seeking behaviors, often preferring traditional medicine or faith healers, and presenting late to facilities, which frustrated health workers.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I observed that because communities are very poor, sometimes people decide to come to the health facility when they are too late for health workers to save their lives. This reflects badly on the health workers because when patients die, they feel that they could have done something to save lives.\u0026rdquo;\u003c/em\u003e (Key informant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;For us when you fall sick, you go to the drugs shops and explain your pains to the attendant, who chooses the drugs, in case things do not work out, you go to the private clinic, there the clinic nurse is more technical than the drug shops attendant who once defeated may refer you to a health center where possibly when your condition worsens, they refer you to hospital.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Our community still strongly prefers traditional medicines, which result in poor seeking behaviors.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Our community of recent has recently turned to prefer \u0026ldquo;balokole\u0026rdquo; (born-again Christians) just to help them pray for conditions like fever, snake bite, diarrhea, serious fall from a tree, because of the perceptions or lack of trust in the usefulness of some medical interventions.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSub-theme: Negative mutual perceptions\u003c/b\u003e: Health workers were criticized for having negative attitudes and providing poor customer care. Conversely, communities were perceived as lacking appreciation and a sense of ownership over local health facilities.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The professional nurses and midwives treat us badly, like we are not human beings. They may pay no attention if someone dies compared with the traditional birth attendants who treat people humanely. In health centres/hospitals, they can slap you and say, ʽTo avoid disturbances, let\u0026rsquo;s do the caesarean operationʼ. This brings fear and scepticism in using the health services.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant quote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;If you have grown up in poverty, you may look older than you actually are, and they will abuse you and say, 'Look at that old woman who has come to give birth.\u0026rdquo;\u003c/em\u003e (FGD participant)\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis mixed-methods study provides a comprehensive analysis of the multi-level factors affecting nurse and midwife performance in a Ugandan district. The quantitative findings demonstrate that both individual attributes (e.g., clinical practice, commitment) and organizational factors (e.g., work environment, systems adherence) are strong, statistically significant predictors of all performance dimensions, explaining a large proportion of the variance (R\u0026sup2; up to 0.951). This aligns with the strategic performance model by Noe et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which posits that individual characteristics and behaviors, shaped by the organizational context, are vital for performance.\u003c/p\u003e \u003cp\u003e The low competency scores (50% in the bottom half) are particularly concerning and are linked to poor clinical practices like not consulting treatment guidelines (C1). This suggests a gap between knowledge and practice, possibly due to inadequate supervision, lack of access to guidelines, or high workload pressures. Similar findings have been reported in other low-resource settings, where clinical decision-making often deviates from standards due to systemic constraints [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This finding resonate with our qualitative data where participants noted challenges in accessing and applying clinical guidelines consistently. These competency gaps likely translate to substandard clinical care and poor patient outcomes, directly affecting the quality of health services delivered.\u003c/p\u003e \u003cp\u003eThe strong predictive power of components related to the work environment and participation (C4) underscores the critical role of organizational health. Unsafe, under-resourced, and non-participatory workplaces are known to lead to burnout, low morale, and high turnover [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our qualitative data vividly describe dilapidated infrastructure, stock-outs, and lack of supportive supervision, creating a context where even motivated workers struggle to perform optimally. This resonates with studies across Sub-Saharan Africa highlighting the demoralizing effect of chronic resource shortages on health workers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prominence of political interference in recruitment and promotion (C2, C6) as a demotivating factor is a critical finding. Decentralization in Uganda aimed to improve local accountability but appears to have facilitated political capture of human resource processes, undermining merit-based systems and professional autonomy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This fosters a climate of unfairness, reduces job security, and can lead to the placement of underqualified personnel, directly impacting service quality [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our qualitative findings directly corroborate this, with participants expressing frustration over political influences in staffing decisions.\u003c/p\u003e \u003cp\u003eThe community-level factors identified-poor health-seeking behaviors and negative attitudes-highlight a reciprocal breakdown in the patient-provider relationship. Communities\u0026rsquo; late presentation and preference for alternative care increase clinical severity and workload, while perceived provider rudeness decreases trust and utilization, creating a vicious cycle [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This lack of social capital and community ownership is a significant barrier to building resilient and responsive health systems [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study's qualitative findings provide first-hand accounts of this dynamic from both health worker and community perspectives.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitation of the study\u003c/h2\u003e \u003cp\u003eOur study has limitations. Its cross-sectional design limits causal inference. The performance measure was self-reported, which may be subject to social desirability bias. However, the use of a validated tool, high reliability scores, and triangulation with qualitative data strengthen validity. The focus on one district may affect generalizability, though the findings likely reflect challenges common to many rural, decentralized districts in Uganda and similar contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications of the study\u003c/h2\u003e \u003cp\u003eThis study has important implications for health services management and policy. For district health managers, our findings highlight actionable areas: insulating recruitment from politics, instituting regular supportive supervision, and creating feedback mechanisms with communities. At the national level, policy revisions are needed to update rigid staffing norms and ensure predictable funding for essential medicines and infrastructure, which are foundational to health worker performance and, ultimately, service quality.\u003c/p\u003e \u003cp\u003eThis study not only identifies key predictors of nurse and midwife performance but also carries important implications for future research. Theoretically, it reinforces the need to adapt existing performance models to account for political and community-level factors in decentralized health systems. Methodologically, it demonstrates the utility of mixed-methods approaches and PCA for distilling complex constructs in health workforce studies. Future studies should employ longitudinal designs to establish causal pathways and evaluate the impact of multi-level interventions on sustained performance improvement.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe performance of nurses and midwives, the frontline of health service delivery in Lira District, is undermined by a confluence of factors across the health system ecosystem. Key issues include poor clinical practices, demotivating work environments, critical resource shortages, politicization of human resource management, and fractured community-health system relationships.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTo improve performance, we recommend:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eStrengthen human resource systems\u003c/strong\u003e \u003cp\u003eCentralize and depoliticize recruitment and promotion processes to ensure meritocracy (e.g., through independent district recruitment panels). Implement transparent, performance-based career development and incentive structures (linked to service delivery metrics).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInvest in the work environment\u003c/strong\u003e \u003cp\u003ePrioritize targeted investments to ensure consistent availability of essential medicines, equipment, and functional infrastructure (including water and power), with district health offices tracking stock-out rates. Develop and disseminate standard operating procedures for all service points, with regular compliance audits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEnhance supervision and support\u003c/strong\u003e \u003cp\u003e Reinvigorate supportive supervision and mentorship focused on clinical competency and adherence to guidelines. Foster participatory leadership and management at facility levels.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEngage communities\u003c/strong\u003e \u003cp\u003eImplement structured community engagement programs to improve health-seeking behaviors, rebuild trust, and foster a sense of co-ownership of local health services.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy revision\u003c/strong\u003e \u003cp\u003eReview the decentralized human resource management framework to curb political interference while maintaining local accountability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAddressing these multi-faceted challenges requires a coordinated, system-strengthening approach from national policymakers, district managers, facility leaders, and communities to empower and enable nurses and midwives to perform to their full potential.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDHIS2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; District Health Information System\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;FGD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Focus Group Discussion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMOH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Ministry of Health\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Principal Component Analysis\u003c/p\u003e\n\u003cp\u003ePNFP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Private Not-For-Profit\u003c/p\u003e\n\u003cp\u003eSDG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003eUHC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Universal Health Coverage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUNMHCP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Uganda National Minimum Health Care Package\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethical approval was granted by the Makerere University School of Public Health Research and Ethics Committee and the Uganda National Council for Science and Technology (Ref: SS 1234). Written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study was conducted as part of an academic degree (MPH). No external funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;EA conceptualized the study, developed methodology, conducted investigation, performed formal analysis, and wrote the original draft. JPB contributed to analysis, writing, review \u0026amp; editing. RK and ER supervised the study, provided resources, and contributed to review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors thank the Ministry of Health Uganda, Makerere University, Lira District Health Office, all health facility in-charges, and the nurses, midwives, and community members who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e¹Department of Health Services, Lira District Local Government, Lira City, Uganda\u003cbr\u003e\u0026nbsp;²School of Public Health, College of Health Sciences, Makerere University, Kampala City, Uganda\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Environmental Health \u0026amp; Disease Control, Faculty of Public Health, Lira University, Lira City, Uganda\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Paying for performance to improve the delivery of health interventions in low- and middle-income countries. Cochrane Database Syst Rev. 2012;2:CD007899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHongoro C, McPake B. How to bridge the gap in human resources for health. Lancet. 2004;364(9443):1451\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilson L. Trust and the development of health care as a social institution. Soc Sci Med. 2003;56(7):1453\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRifkin SB. Lessons from community participation in health programmes: a review of the post Alma-Ata experience. Int Health. 2009;1(1):31\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health workforce, Performance, Nurses, Midwives, Uganda, Health systems, Mixed-methods","lastPublishedDoi":"10.21203/rs.3.rs-8513826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8513826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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\u0026thinsp;=\u0026thinsp;30) were analyzed thematically. All qualitative findings reported are based exclusively on primary data collected during the study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe majority of respondents were female (83.3%), certificate holders (72.4%), and had 1\u0026ndash;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\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), participation in decision-making (C4), and unfavorable working conditions (C4, β\u0026thinsp;=\u0026thinsp;0.120, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Linear regression showed components C1, C2, C3, C4 \u0026amp; C5 significantly predicted overall performance (R\u0026sup2;=0.882, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Most nurses had competency levels between 0\u0026ndash;50%, while productivity, availability, and responsiveness levels were between 51\u0026ndash;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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIndividual 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.\u003c/p\u003e","manuscriptTitle":"Predictors of nurse and midwife performance in a Ugandan district: a mixed-methods study of individual, organizational, and community factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 20:06:21","doi":"10.21203/rs.3.rs-8513826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T06:11:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T08:40:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T08:24:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173357750190632500016675474841671827270","date":"2026-02-05T07:46:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63012935581075404123800834733002119635","date":"2026-01-27T06:25:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-26T23:29:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T22:52:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T08:51:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T08:15:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-01-20T07:58:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f77e6476-af24-473e-bdaf-2b51c44713b7","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T03:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 20:06:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8513826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8513826","identity":"rs-8513826","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}