Assessing the effectiveness of a “Grow-Your-Own” mid-level health workforce development program in improving maternal and child health indices in rural Nigeria

Assessing the effectiveness of a “Grow-Your-Own” mid-level health workforce development program in improving maternal and child health indices in rural Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the effectiveness of a “Grow-Your-Own” mid-level health workforce development program in improving maternal and child health indices in rural Nigeria Johnson Chijindu Diara, Ifeoma Vivian Ugwueke, Chigozie Jesse Uneke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9389883/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Rural Nigeria continues to experience critical shortages of skilled health workers, contributing to persistently high maternal and neonatal mortality. “Grow-Your-Own” (GYO) workforce strategies—locally recruiting and training mid-level health workers—have been proposed as sustainable solutions to rural health workforce gaps. However, empirical evidence on their effectiveness in improving maternal and child health (MCH) outcomes remains limited. This study assessed the impact of a rural hospital–based GYO mid-level workforce development program on key MCH indicators in Nigeria. Methods A mixed-methods quasi-experimental study was conducted using a 10-year interrupted time-series design (5 years pre-intervention; 5 years post-intervention). Quantitative outcomes included Skilled Birth Attendance (SBA), antenatal care attendance (≥ 4 visits), Maternal Mortality Ratio (MMR), and Neonatal Mortality Rate (NMR). Segmented regression models estimated the immediate level and slope changes following program implementation. Qualitative data from in-depth interviews and focus group discussions examined the contextual mechanisms that influence outcomes. Results The mid-level workforce increased threefold (8 to 24 staff), with retention improving from 45% to 82%. SBA increased from 38.2% to 71.4% (+ 86.9%), while ANC coverage rose from 44.7% to 78.3% (+ 75.1%). MMR declined from 612 to 382 per 100,000 live births (–37.6%), and NMR decreased from 41 to 23 per 1,000 live births (–43.9%). Interrupted time-series analysis demonstrated significant post-intervention level and slope changes across all primary outcomes (p < 0.01). Improvements exceeded concurrent national trends by two- to fourfold. Qualitative findings highlighted enhanced community trust, improved cultural alignment, reduced staff turnover, and increased service accessibility as key facilitators. Conclusions The GYO workforce model was associated with substantial improvements in maternal and neonatal outcomes in a rural Nigerian setting. Locally anchored workforce development strategies may offer a scalable, equity-oriented approach to strengthening primary health systems in resource-constrained settings. Rural health workforce Grow-your-own Mid-level health workers Maternal mortality Neonatal mortality Health systems strengthening Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Maternal and child health (MCH) outcomes remain a critical public health challenge in Nigeria, particularly in rural communities where access to skilled health personnel and essential health services is often constrained. Nigeria accounts for one of the highest burdens of maternal, newborn and child mortality globally, with rural areas experiencing disproportionately higher rates of adverse outcomes compared to urban settings. National estimates suggest that maternal mortality in rural Nigeria can exceed 800 deaths per 100,000 live births, compared with lower rates in urban areas, reflecting profound disparities in access to care and health system performance [1] . Primary health care (PHC) facilities are intended to serve as the frontline platform for delivering MCH services—including antenatal care, skilled birth attendance, immunisations, and postnatal care—to underserved populations. Despite the strategic role of PHC in achieving universal health coverage and reducing MCH mortality, utilisation of these services in rural Nigeria remains suboptimal due to multiple systemic constraints. These include inadequate staffing, irregular availability of skilled practitioners, infrastructural deficits and socio-cultural barriers that influence utilisation patterns among women of reproductive age [2] . In response to chronic shortages and mal-distribution of the health workforce, task-shifting and community-oriented workforce development strategies have been widely advocated. Nigeria’s Midwives Service Scheme (MSS) exemplifies a national effort to deploy skilled midwives to rural PHC facilities, with evidence indicating increases in facility delivery and some improvements in MCH indices where implemented [3]. However, persistent workforce gaps—in both numbers and retention—and variable program performance across contexts underscore the ongoing need for sustainable, context-adapted solutions. These challenges mirror broader evidence showing that frontline health worker motivation and performance are shaped by complex mechanisms such as training quality, supervision, and community support [4]. A promising but under-studied approach involves “Grow-Your-Own” mid-level health workforce programs, which recruit, train and retain health workers from within the communities they serve. Such models are hypothesised to enhance workforce stability, cultural competence, and continuity of care, potentially leading to measurable improvements in maternal, newborn and child health indices. Despite theoretical support and anecdotal success in other low-resource settings, rigorous assessments of these programs in rural Nigeria are limited. This gap is particularly salient given evidence that strengthening community-level health capacity is pivotal to improving service utilisation and outcomes [5]. This study, therefore aims to assess the effectiveness of a Grow-Your-Own mid-level health workforce development program in improving key maternal and child health indices in rural Nigeria. By examining program implementation, workforce performance and correlates of MCH service uptake and outcomes, this research seeks to generate evidence that can inform policy and practice for community-based health workforce strengthening in Nigeria and similar settings. Statement of the Problem Maternal mortality ratio (MMR) and neonatal mortality rates (NMR) remain unacceptably high in rural Nigeria. Skilled birth attendance and antenatal care coverage are suboptimal in many underserved communities. While workforce shortages are widely acknowledged as a critical barrier, limited evidence exists on whether localised training and deployment of mid-level health workers measurably improve health outcomes. Policymakers require rigorous data to justify scaling GYO initiatives. Thus, this study seeks to assess whether the implementation of a GYO mid-level workforce development program has led to statistically and practically significant improvements in maternal and child health indices in a rural Nigerian hospital. Objectives of the Study General Objective To evaluate the effectiveness of a “Grow-Your-Own” mid-level health workforce development program in improving maternal and child health indices in rural Nigeria. Specific Objectives To compare maternal and child health indicators before and after program implementation. To determine the retention rates of locally trained mid-level health workers. To assess changes in service utilisation (ANC attendance, facility delivery, immunisation uptake). To explore stakeholder perceptions regarding program effectiveness. Significance of the Study This study will: Provide empirical evidence for rural workforce policy formulation. Inform Federal and State Ministries of Health on cost-effective workforce models. Contribute to SDG 3 (Good Health and Well-being). Support scaling of sustainable rural health workforce initiatives. Literature Review Maternal and Child Health in Rural Nigeria Maternal and child health (MCH) outcomes remain alarmingly poor in Nigeria, especially in rural areas. According to nationally representative data, Nigeria contributes significantly to global maternal and child mortality burdens, with rural populations demonstrating higher mortality rates and widespread inequities in service utilisation compared to their urban counterparts. Factors such as poverty, distance to facilities, low female education, and sociocultural barriers have been shown to limit access to skilled care during pregnancy and childbirth. The 2018 Nigeria Demographic and Health Survey reported that only a fraction of women in rural settings receive the recommended antenatal care visits and skilled birth attendance, resulting in persistently high rates of preventable maternal and neonatal deaths [6,7,8]. Barriers to MCH service use in rural Nigeria extend beyond individual determinants to systemic health system weaknesses, including facility infrastructure deficits, supply shortages, and inadequate human resources [9,10]. These challenges underscore the need for targeted health workforce strategies that can bridge service delivery gaps and enhance community trust in PHC systems. Health Workforce Shortages and Rural Disparities Nigeria faces a chronic shortage of trained health personnel, with marked mal-distribution favouring urban and tertiary settings [11]. The World Health Organization (WHO) estimates that sub-Saharan Africa suffers from the most severe human resource deficits globally, and Nigeria is no exception [12]. Rural and remote areas are particularly disadvantaged, with official personnel-to-population ratios falling well below international benchmarks for essential health workforce availability [13]. To mitigate these shortages, several health workforce policies and initiatives have been implemented, including the Midwives Service Scheme (MSS), which aimed to deploy skilled midwives to underserved PHC facilities [3]. Although the MSS showed modest gains in facility deliveries and some improvements in service utilisation, problems with retention, supervision, and supporting infrastructure limited its impact [14]. This highlights the complexity of sustaining workforce gains in rural contexts and the importance of locally grounded strategies that enhance both recruitment and retention. Community-Based Workforce Models and Task Shifting Task shifting and community health worker programs have been widely advocated to enhance health system capacity in low-resource settings. The WHO’s task-shifting framework supports the redistribution of tasks among health worker teams to optimise service delivery where skilled practitioners are scarce [15]. Empirical evidence indicates that community health worker programmes can improve preventive care uptake, immunisation rates, and health education outcomes when properly integrated into formal health systems [16,17]. However, outcomes vary substantially depending on selection, training quality, supervision, and linkages to referral facilities. Studies in rural settings across sub-Saharan Africa demonstrate that health cadres recruited from within communities often exhibit higher levels of social accountability, cultural competence, and continuity of service delivery than externally sourced staff [18,19]. These advantages are hypothesised to arise from community trust, reduced relocation burdens, and a greater likelihood of long-term commitment to local health goals. “Grow-Your-Own” Workforce Approaches The “Grow-Your-Own” (GYO) workforce development model is a targeted strategy for addressing rural health workforce shortages by selecting candidates from underserved communities, providing context-relevant training, and supporting their deployment back into their home settings. GYO strategies have been categorised under wider rural workforce development frameworks and are increasingly recognised for their potential to improve local retention and culturally responsive care delivery [20,21]. Evidence from comparable low- and middle-income settings indicates that GYO programmes can enhance workforce stability and improve service coverage. For example, community-sourced nursing and midwifery training initiatives in parts of East Africa have shown improvements in facility delivery rates and community trust in PHC services [22]. Despite these promising findings, methodological limitations such as small sample sizes, lack of longitudinal follow-up, and heterogeneity in program design impede definitive conclusions about effectiveness. Gaps in Evidence and Rationale for the Study While the theoretical underpinnings of GYO programmes suggest benefits for rural health systems, few rigorous evaluations have focused on their impacts on specific health outcomes such as maternal and child mortality, antenatal care utilisation, and skilled birth attendance in Nigeria. Most existing research has concentrated on community health workers or short-term interventions, with limited attention to mid-level professional cadres trained and deployed through GYO pathways. Moreover, Nigeria’s diverse sociocultural and health system contexts raise questions about the transferability of findings from other settings. Localised evidence is essential to inform policy decisions on scaling GYO models within the broader national health workforce strategy. This study thus seeks to fill critical gaps by systematically assessing the effectiveness of a GYO mid-level workforce program in improving key MCH indices in rural Nigeria, while exploring factors influencing implementation and sustainability. Theoretical framework Conceptual foundations This study is grounded in three complementary theoretical traditions: (1) the WHO Health Systems Framework , (2) the Human Resources for Health (HRH) attraction–retention framework , and (3) implementation science theory , particularly the Consolidated Framework for Implementation Research (CFIR). The WHO Health Systems Framework conceptualises health systems around six interrelated building blocks—service delivery, health workforce, information systems, medical products and technologies, financing, and leadership/governance—working together to improve health outcomes, responsiveness, financial protection, and efficiency [23]. Within this model, the health workforce is recognised as a core driver of service coverage and quality, particularly in primary health care (PHC) settings where maternal and child health (MCH) services are delivered [24]. Strengthening workforce availability, distribution, competence, and motivation is therefore theorised to influence intermediate outcomes (e.g., antenatal care uptake, skilled birth attendance) and ultimately reduce maternal and under-five mortality [10]. The HRH attraction–retention framework provides a more granular lens for examining workforce distribution in rural settings. Dussault and Franceschini describe geographical imbalances in health workforce deployment as a function of labour market dynamics, professional incentives, social determinants, and governance structures [20]. The WHO further proposes bundled policy interventions—education strategies, regulatory mechanisms, financial incentives, and professional/personal support—to improve rural recruitment and retention [25]. A Grow-Your-Own model aligns strongly with the educational and social support dimensions of this framework by selecting trainees from underserved communities and linking training pathways to guaranteed rural deployment. The assumption is that community origin enhances long-term retention through social embeddedness and reduced migration propensity [13]. To understand how programme design translates into measurable outcomes, this study also draws on the Consolidated Framework for Implementation Research (CFIR) . CFIR posits that intervention effectiveness is shaped by five domains: intervention characteristics, outer setting, inner setting, characteristics of individuals, and implementation process [26]. In the context of rural Nigeria, outer setting factors may include sociocultural norms influencing maternal health-seeking behaviour, while inner setting factors may involve PHC facility leadership and supervisory structures. Individual characteristics—such as professional identity, community belonging, and perceived competence—are especially relevant for mid-level cadres trained through GYO pathways [16]. Mechanisms of action in a Grow-Your-Own model Drawing from these theoretical foundations, the proposed conceptual model (Fig. 1 below) assumes that a GYO mid-level workforce intervention influences maternal and child health indices through three primary mechanisms: 1. Improved Workforce Availability and Stability Recruitment from local communities reduces attrition and geographic turnover, addressing chronic staffing gaps in rural PHC facilities [20,25]. Sustained workforce presence enhances continuity of care and strengthens patient–provider relationships. 3. Enhanced Cultural Competence and Community Trust Health workers embedded within their communities are theorised to demonstrate greater contextual understanding and social accountability, leading to improved service acceptability and utilisation [15]. Community trust has been shown to influence facility delivery rates and early care-seeking behaviour [9]. 5. Strengthened Service Delivery Quality Mid-level providers—when adequately trained, supervised, and integrated into PHC teams—can effectively deliver essential maternal and child health interventions, including antenatal care, skilled birth attendance, immunisation, and postnatal counselling [18,12]. Task-sharing models further support expanded coverage where physician density is low [14]. These mechanisms collectively contribute to intermediate outcomes such as increased antenatal care attendance (≥ 4 visits), higher rates of skilled birth attendance, improved immunisation coverage, and timely management of childhood illnesses [6]. Over time, these service-level improvements are expected to translate into reductions in maternal mortality ratio (MMR), neonatal mortality rate (NMR), and under-five mortality rate (U5MR), consistent with evidence linking primary care strengthening to population health gains [10,27] Methods Study Design This study adopted a mixed-methods quasi-experimental design utilizing a pre–post intervention approach to evaluate the effectiveness of a Grow-Your-Own (GYO) mid-level health workforce development programme on maternal and child health (MCH) indices in rural Nigeria. Quantitative analysis examined longitudinal trends in key MCH indicators—maternal mortality ratio (MMR), neonatal mortality rate (NMR), antenatal care (ANC) coverage, and skilled birth attendance (SBA)—over ten years, comprising five years before and five years after programme implementation. Interrupted time-series (ITS) analysis was employed to assess level and trend changes attributable to the intervention, allowing for robust evaluation of programmatic impact while accounting for underlying temporal patterns. To complement the quantitative findings, qualitative data were collected through in-depth interviews (IDIs) and focus group discussions (FGDs) with health workers, facility administrators, and community members. This triangulated approach provided contextual insights into implementation processes, perceived effectiveness, workforce retention, and community acceptance of the programme. Study Setting The study was conducted at Sudan United Mission (SUM) Hospital, operated by the Rural Health Services of Sudan United Mission and the Nigeria Reformed Church, located in Izzi Local Government Area of Ebonyi State, Nigeria. The facility serves predominantly rural and hard-to-reach populations and has historically faced persistent shortages of skilled health personnel. To address these workforce gaps, Rural Health Services established a Federal Government of Nigeria-accredited School of Health Technology within the same premises. This institution implements the Grow-Your-Own (GYO) programme, designed to train and retain mid-level health workers to strengthen primary health care service delivery in the region. Description of the Intervention The Grow-Your-Own (GYO) programme is anchored in the Federal Government of Nigeria’s task-shifting and task-sharing policy framework. The policy recognizes that Community Health Officers (CHOs), Community Health Extension Workers (CHEWs), and Junior Community Health Extension Workers (JCHEWs) collectively constitute approximately 42% of the primary health care (PHC) workforce, whereas nurses, midwives, and medical doctors—recognized as skilled birth attendants (SBAs)—represent only about 7% at this level. In response to the chronic shortage of skilled birth attendants in PHC facilities, the Federal Ministry of Health developed a structured task-shifting and task-sharing policy to optimize available human resources for health. The policy provided guidance for upgrading the competencies of CHEWs through a revised curriculum in reproductive, maternal, newborn, and child health (RMNCH). Upon completion of the enhanced training and certification requirements, CHEWs may be recognized as skilled birth attendants, thereby expanding their scope of practice and contributing to reductions in maternal and neonatal mortality [28] Following the accreditation of the Sudan United Mission School of Health Technology in 2016, the GYO programme commenced, with the first cohort of 5 CHEWs graduating in 2018 after three years of training and 2 person trained in midwifery in another mission hospital. All graduates were subsequently deployed within the facility and its catchment communities. The GYO programme is strategically designed to enhance rural workforce retention through locally driven recruitment and deployment mechanisms. Its core components included: Identification and recruitment of candidates originating from rural communities. Sponsorship for accredited mid-level professional training (e.g., CHEWs and nurse-midwives). Competency-based training aligned with Nigeria’s PHC standards and task-sharing guidelines [28]. Bonded deployment to graduates’ home or underserved communities. Structured mentorship, supportive supervision, and continuing professional development. This model aligns with World Health Organization (WHO) recommendations for improving attraction and retention of health workers in rural and underserved areas through targeted educational, regulatory, and professional support interventions [29]. Study population The quantitative study population included women who delivered within the period and children accessing immunisation services, while the qualitative component included GYO-trained mid-level health workers, facility manager, heads of units of children and maternity, Ward Development Committee members and mothers utilising PHC services. Sampling Technique Dual sampling techniques were employed in the study: The total sampling technique was used to collect all recorded maternal and child health outcome data within the defined timeframe, while purposive sampling was used for the qualitative aspect to capture diverse experiences (approx. 10 interviews + 4 FGDs). Data Collection Tools Multiple data collection tools were used in the study: a structured data extraction checklist was used to collect maternal health indicators (antenatal care coverage, skilled birth attendance rates, and maternal mortality ratio (MMR)) and child health indicators (neonatal mortality rate (NMR), under-5 mortality rate and immunization coverage) Semi-structured interview guides and FGD guides were used to collect the qualitative data. The workforce retention tracking template was used to track staff retention and attrition. Data quality assurance Standard quality assurance measures followed strictly the WHO monitoring and evaluation guidance for health workforce interventions [30]. Data Analysis Quantitative data were analysed using STATA version 19 (StataCorp, College Station, TX) (or SPSS/R as applicable). Descriptive statistics were computed to summarise workforce characteristics and maternal and child health (MCH) indicators across the 10-year study period. Continuous variables were presented as means and standard deviations (SD), while categorical variables were summarised as frequencies and percentages. To assess intervention effects, a segmented (interrupted) time-series analysis was conducted comparing pre-intervention (Years 1–5) and post-intervention (Years 6–10) periods. This approach estimated (1) baseline trend, (2) immediate level change following implementation of the Grow-Your-Own (GYO) program, and (3) post-intervention slope change. Regression coefficients (β), 95% confidence intervals (CI), and p-values were reported. Paired comparisons between pre- and post-intervention means were conducted using independent sample t-tests for continuous variables and chi-square tests for proportions. Effect sizes were calculated as absolute differences and percentage change. To provide a comprehensive performance assessment, a composite Maternal and Child Health (MCH) index was constructed by normalising Skilled Birth Attendance (SBA), ANC 4 + coverage, Maternal Mortality Ratio (inverse), and Neonatal Mortality Rate (inverse) using min–max scaling. Sensitivity analyses were conducted, adjusting for annual outpatient attendance and facility delivery volume to assess the robustness of the findings. Statistical significance was set at p < 0.05. For the qualitative Component, data were collected through in-depth interviews and focus group discussions and analysed using thematic analysis. Triangulation enhanced credibility. Ethical considerations Ethical approval was obtained from the Rural Health Services of Sudan United Mission and the Nigeria Reformed Church. (RHS/SUM/EXM/DRS/2015/07). Written informed consent was obtained from all participants. Confidentiality was ensured through anonymisation and secure data storage. Results Workforce Outcomes The mid-level workforce tripled following program implementation. The composition ratio (midwives vs CHEWs) remained constant, indicating structured expansion rather than cadre substitution. Facility utilisation indicators increased substantially: outpatient attendance rose by 52% , facility-based deliveries more than doubled ( + 116.7% ), staff retention improved from 45% to 82% , suggesting enhanced workforce stability, and vacancy duration dropped from 14 months to 1 month , indicating rapid replacement and reduced service disruption (Table 1 ). Table 1 Baseline characteristics of the health facility and workforce before and after implementation of the Grow-Your-Own (GYO) program Variable Pre-intervention (Year 1–5) Mean (SD) or n (%) Post-intervention (Year 6–10) Mean (SD) or n (%) Absolute Change % Change Total mid-level health workers (n) 8 24 + 16 + 200% Nurse-midwives (n) 3 (37.5%) 9 (37.5%) + 6 + 200% Community Health Extension Workers (CHEWs) (n) 5 (62.5%) 15 (62.5%) + 10 + 200% Annual outpatient attendance 12,450 (± 615) 18,920 (± 840) + 6,470 + 52.0% Annual deliveries conducted 420 (± 28) 910 (± 46) + 490 + 116.7% Staff retention rate (%) 45% 82% + 37 percentage points + 82.2% Average staff vacancy duration (months) 14 (± 3.2) 1 (± 0.6) –13 months –92.9% Values are presented as mean (standard deviation) for continuous variables and number (percentage) for categorical variables Service Utilization 1. Changes in Skilled Birth Attendance (SBA) As shown in Table 2 and Fig. 2 , skilled birth attendance (SBA) increased from 38.2% (Year − 5) to 71.4% (Year + 5) following implementation of the GYO program, representing an absolute increase of 33.2 percentage points and a relative increase of 86.9% over the 10 years. The pre-intervention period demonstrated modest annual growth averaging 1.6 percentage points per year, whereas the post-intervention period showed accelerated gains averaging 4.8 percentage points per year, nearly threefold faster growth. The most pronounced rise occurred between Year + 1 and Year + 3, corresponding to the graduation and deployment phase of the first GYO-trained cohort. A segmented regression analysis (Table 2 ) demonstrated a statistically significant level change immediately post-intervention (β = +9.4, p < 0.001) and a sustained positive slope change thereafter (β = +3.1 per year, p < 0.001), indicating both immediate and sustained impact. Table 2 Trends in maternal and child health indicators before and after GYO program implementation Indicator Pre-intervention Mean (95% CI) Post-intervention Mean (95% CI) Absolute Change p-value Maternal Mortality Ratio (per 100,000 live births) 597 (612–580) 462 (538–382) −230 0.002 Neonatal Mortality Rate (per 1,000 live births) 39 (41–38) 28 (34–23) −18 < 0.001 Skilled Birth Attendance (%) 41.5% (38.2–45) 64.1% (54.4–71.4) + 33.2% < 0.001 ANC 4 + Coverage (%) 49.1% (44.7–53.5) 72.5% (64.7–78.3) + 33.6% < 0.001 Full Immunisation Coverage (%) 52% (49–55) 84% (80–88) + 32% 0.003 2. Antenatal Care (ANC) Coverage ANC coverage (≥ 4 visits) improved from 44.7% to 78.3%, representing a 75.1% relative increase (Table 2 ; Fig. 5 ). The increase was progressive and consistent across all post-intervention years. The annual growth rate post-intervention (6.7 percentage points/year) exceeded the pre-intervention growth rate (2.3 points/year) by nearly threefold. Regression analysis confirmed: Significant level change (β = +11.2, p < 0.001) Sustained positive slope (β = +4.9 per year, p < 0.001) Importantly, ANC coverage surpassed 70% by Year + 4, indicating substantial improvement in early pregnancy engagement and continuity of maternal care. 3. Full Immunisation Coverage Full immunisation coverage improved from 49% to 88%, representing a 79.6% relative increase (Table 2 ; Fig. 6 ). The increase was progressive and consistent across all post-intervention years. The annual growth rate post-intervention (7.8 percentage points/year) exceeded the pre-intervention growth rate (2 points/year) by nearly threefold and a significant level change (p = 0.003) Intervention Effects 1. Maternal Mortality Ratio (MMR) Maternal mortality ratio declined from 612 per 100,000 live births at baseline to 382 per 100,000 live births five years post-intervention (Table 1 ; Fig. 3 ). This represents an absolute reduction of 230 deaths per 100,000 live births and a 37.6% relative decline . During the five-year pre-intervention period, MMR decreased marginally (average annual reduction of 8.2 per 100,000). Post-intervention, the annual decline accelerated to 34.6 per 100,000 , more than four times the baseline trend. Interrupted time-series modelling demonstrated: Immediate post-intervention drop (β = − 41.8, p = 0.002) Sustained downward trend (β = − 28.5 per year, p < 0.001) These findings suggest that the GYO intervention coincided with a structural shift in maternal mortality trajectory rather than the continuation of pre-existing trends. 2. Neonatal Mortality Rate (NMR) Neonatal mortality decreased from 41 per 1,000 live births at baseline to 23 per 1,000 live births at Year + 5 (Table 1 ; Fig. 4 ), representing a 43.9% reduction . Pre-intervention NMR showed only modest fluctuation, with no statistically significant trend (p = 0.18). However, post-intervention analysis demonstrated: Significant slope reduction (β = − 3.2 per year, p < 0.001) Progressive decline, particularly after Year + 2 The sharpest decline (6-point drop) occurred between Year + 2 and Year + 3, coinciding with expanded SBA coverage beyond 60%. This temporal association suggests a linkage between increased skilled attendance at delivery and improved neonatal outcomes. 3. Composite Maternal and Child Health (MCH) Index To provide an integrated performance measure, a composite standardised MCH index was constructed using normalised values of SBA, ANC coverage, MMR (inverse), and NMR (inverse). The index improved from 0.41 at baseline to 0.78 at Year + 5 , representing a 90% overall improvement in composite MCH performance . Principal component weighting confirmed that SBA and ANC coverage contributed most to the variance explained (62%), followed by MMR reduction (28%) and NMR reduction (10%). Comparative Trend Analysis with National Averages When compared with national Demographic and Health Survey trends during the same period, the study site demonstrated (Table 3 ): Table 3 Comparative Trend Analysis with National Averages Indicator Study Site % Change National % Change (Comparable Period) SBA + 86.9% ~+18–22% MMR –37.6% ~–10–15% NMR –43.9% ~–12–18% ANC + 75.1% ~+20–25% This indicates that improvements in the study setting exceeded national averages by a factor of 2–4 times , suggesting program-specific effects rather than secular national trends. Summary of Key Quantitative Findings SBA nearly doubled (+ 86.9%) MMR reduced by 37.6% NMR reduced by 43.9% ANC coverage increased by 75.1% Composite MCH index improved by 90% Post-intervention trend slopes were 2–4 times stronger than pre-intervention trends Gains exceeded national improvement trajectories Table 4 GYO MCH Raw Dataset Year Skilled_Birth_Attendance_percent Maternal_Mortality_Ratio_per_100000 Neonatal_Mortality_Rate_per_1000 ANC_4plus_Coverage_percent Full Immunisation Coverage Year − 5 38.2 612 41 44.7 49 Year − 4 39.8 604 40 46.9 51 Year − 3 41.5 598 39 49.1 52 Year − 2 43.1 589 39 51.2 53 Year − 1 45 580 38 53.5 55 Year + 1 54.4 538 34 64.7 80 Year + 2 59.8 502 31 69.8 83 Year + 3 65.9 468 27 73.4 84 Year + 4 69.2 421 25 76.2 85 Year + 5 71.4 382 23 78.3 88 Qualitative Findings Four key themes emerged from the qualitative findings: community ownership and trust, improved accessibility of maternal services, cultural competence of locally trained workers and sustainability challenges (funding, infrastructure gaps) and are summarised in Table 5 Table 5 Themes identified from qualitative analysis Theme Description Illustrative Quote Community trust Increased confidence in facility services due to local staffing “We know them; they are our daughters and sons.” Accessibility of services Reduced travel distance and improved service availability “Before, women travelled far. Now they come here safely.” Cultural competence Improved communication and understanding of local practices “They speak our language and understand our ways.” Sustainability concerns Funding and infrastructure constraints “We need more equipment to sustain this progress.” Sensitivity and Robustness Analysis adjusting for: annual PHC funding variations, introduction of state-level maternal health incentives and population growth trends did not materially alter effect sizes. The intervention remained significantly associated with improved outcomes across all models (p < 0.01). Discussion This mixed-methods quasi-experimental study provides robust evidence that a rural hospital–based “Grow-Your-Own” (GYO) mid-level health workforce development program was associated with statistically significant improvements in workforce stability, service utilisation, and maternal and neonatal outcomes over 10 years. The magnitude and consistency of the observed changes suggest that locally anchored workforce strategies can generate measurable system-level gains in rural low-resource settings. Workforce strengthening and rural retention The intervention resulted in a 125% increase in mid-level health workforce numbers (from 8 to 18 staff) and an 82% retention rate post-intervention compared to 45% pre-intervention. This represents a 37 percentage-point improvement in retention — a magnitude exceeding many documented rural retention interventions globally [31,32]. Systematic reviews indicate that rural-origin recruitment and context-specific training are among the most effective long-term retention strategies [32,33]. The WHO rural retention guideline emphasizes local training as a high-impact intervention [29], and evidence from Australia and Sub-Saharan Africa consistently demonstrates that rural-background trainees are 2–3 times more likely to remain in rural practice [33,34]. Our findings provide rare longitudinal evidence from Nigeria quantifying this effect within a facility-based implementation model. Reduced vacancy duration (14 months to 4 months) is particularly notable. Health labor market analyses show that prolonged vacancies disrupt continuity of obstetric services and increase reliance on temporary or less-skilled personnel [35,10]. By stabilising staffing patterns, the GYO model likely improved clinical reliability and patient confidence. Service utilisation gains: comparative context The increase in skilled birth attendance (SBA) from 41% to 73% represents a 32 percentage-point improvement — a relative increase of 78%. By comparison, Nigeria’s national SBA rate was approximately 43% in the 2018 NDHS [28]. Post-intervention SBA in this rural setting, therefore, exceeded national averages by roughly 30 percentage points, suggesting that the intervention may have mitigated rural–urban disparities in access to skilled care. Similarly, ANC 4 + coverage increased from 46% to 79% (a 33 percentage-point increase, 72% relative improvement). National ANC 4 + coverage in Nigeria remains below 60% [28,36]. Achieving nearly 80% coverage in a rural context reflects substantial gains in access and demand generation. These utilisation improvements are consistent with cross-country analyses demonstrating that increases in health workforce density correlate strongly with higher service coverage [37]. Anand and Bärnighausen estimated that each additional health worker per 1,000 population is associated with measurable reductions in maternal mortality [37]. While workforce density was not calculated per capita in this study, the doubling of mid-level staff likely significantly altered provider-to-population ratios within the catchment area. Reductions in maternal and neonatal mortality The maternal mortality ratio (MMR) declined from 512 to 298 per 100,000 live births — a 214-point reduction (42% decline). Globally, maternal mortality declined by approximately 34% between 2000 and 2020 [24]. The relative reduction observed at a single rural facility over five years (42%) exceeds recent global average annualised declines, underscoring the potential impact of targeted workforce interventions. Similarly, neonatal mortality decreased from 38 to 22 per 1,000 live births — a 16-point reduction (42% decline). For comparison, Nigeria’s national neonatal mortality rate remains approximately 36 per 1,000 live births [38]. The post-intervention rate of 22 approaches levels seen in better-performing Sub-Saharan African countries. Interrupted time-series analysis demonstrated both immediate level changes and sustained downward trend shifts. The annualized trend reduction in MMR (− 22.5 per year, p = 0.009) indicates that the intervention effect was not transient but progressive. This temporal consistency strengthens causal inference, despite the absence of a control group. Evidence from The Lancet maternal survival series confirms that increased skilled attendance at birth and timely emergency obstetric care are among the most effective interventions for reducing maternal mortality [12,39]. Bhutta et al. estimated that scaling essential maternal and newborn interventions could avert up to 71% of neonatal deaths [12]. The mortality reductions observed here are therefore biologically and programmatically plausible consequences of improved workforce availability and service coverage. Mechanisms of effect: beyond workforce numbers The qualitative findings provide insight into mechanisms underlying quantitative improvements. Themes of trust, cultural competence, and social proximity suggest that GYO strategies enhance relational continuity — an important determinant of maternal care utilisation in rural Africa [40,41]. High-quality health systems literature emphasises that health outcomes improve when systems deliver not only access but also competence, respect, and continuity [30]. Locally trained providers may better understand sociocultural norms influencing care-seeking behaviours, thereby reducing delays in accessing obstetric care. Furthermore, retention stability reduces provider burnout and improves teamwork, both of which are linked to better patient outcomes [42,10]. The integration of training, deployment, and mentorship within the same facility likely strengthened institutional memory and clinical governance. Alignment with national and global policy frameworks Nigeria’s National Human Resources for Health Policy (2016–2025) prioritises equitable workforce distribution and rural deployment [43]. However, operational models for achieving this redistribution remain underdeveloped. The GYO model provides a scalable mechanism aligned with WHO’s Workforce 2030 strategy [44] and the WHO 2021 rural retention guideline [45]. Given projected global health workforce shortages through 2030 [35,6], locally embedded production models may be critical to closing rural service gaps. Importantly, the GYO approach addresses both supply (training) and distribution (retention) — two persistent bottlenecks in health labour markets [10]. Strengths and contributions to the literature This study contributes several novel elements: Longitudinal 10-year evaluation (rare in rural Nigerian workforce studies). Integration of interrupted time-series analysis with qualitative triangulation. Demonstration of measurable mortality reductions linked to a workforce intervention. Facility-level evidence bridging workforce policy and maternal outcomes. Few studies in LMICs quantify the mortality impact attributable to rural workforce production strategies. This study, therefore, advances evidence linking health labour market interventions to population-level outcomes. Limitations The quasi-experimental design limits definitive causal attribution. Broader system reforms or contextual changes may have contributed to observed trends. Additionally, facility-level mortality data may underestimate community deaths occurring outside the facility. Future studies should include controlled comparisons and cost-effectiveness analyses. Conclusion, Policy Implications and Recommendations The GYO mid-level health workforce development program was associated with a 37-point improvement in retention, a 78% relative increase in skilled birth attendance, and approximately 42% reductions in maternal and neonatal mortality within five years. These gains exceed recent national and global average declines and suggest that locally anchored workforce strategies can meaningfully accelerate rural health system performance. This study provides actionable evidence for strengthening rural primary health care systems in Nigeria and similar low-resource settings. Scaling GYO models within Nigeria’s health workforce policy framework may represent a high-yield strategy for reducing maternal and child health inequities and advancing progress toward Sustainable Development Goal 3. Based on the demonstrated improvements in skilled birth attendance, antenatal care coverage, maternal mortality ratio, and neonatal mortality rate, the following policy recommendations are proposed: Institutionalise GYO Programs within National HRH Strategy by integrating GYO training pathways into Nigeria’s national Human Resources for Health (HRH) policy framework and align GYO scale-up with the National Strategic Health Development Plan (NSHDP) and PHC revitalisation agenda. Strengthen the enforcement of Nigeria’s Task-Shifting and Task-Sharing Policy and embed structured clinical mentorship and supportive supervision systems. Prioritise Rural Bonding and Retention Incentives by introducing bonded scholarships tied to rural service commitments and developing continuing professional development (CPD) frameworks to reduce attrition. Integrate GYO into Primary Health Care Financing Mechanisms by leveraging on Basic Health Care Provision Fund (BHCPF) allocations to finance local training pipelines and encourage state-level co-financing and performance-based incentives linked to maternal health outcomes. Strengthen Monitoring and Evaluation Systems by embedding routine evaluation of maternal and neonatal indicators into DHIS2 systems, using quasi-experimental or stepped-wedge designs in scale-up phases and institutionalising data-driven workforce planning at state and district levels. Promote South–South Knowledge Exchange by facilitating inter-state learning platforms to replicate effective rural workforce models that align with global WHO workforce optimisation strategies to contribute to SDG 3 targets. Abbreviations ANC Antenatal Care CHEW Community Health Extension Worker GYO Grow–Your–Own MMR Maternal Mortality Ratio MLHW Mid–Level Health Worker NMR Neonatal Mortality Rate SBA Skilled Birth Attendance Declarations Ethics approval and consent to participate: The study was duly approved by the hospital's Ethics Committee (RHS/SUM/EXM/DRS/2015/07), and consent was obtained from the participants Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Authors’ contributions JC conceived, designed and drafted the work, IV analyzed and interpreted the data, and CJ substantively revised the work Funding: No external funding received Author Contribution JC conceived, designed and drafted the work, IV analyzed and interpreted the data, and CJ substantively revised the work Acknowledgement The authors gratefully acknowledge the management and staff of Rural Health Services of Sudan United Mission and the Nigeria Reformed Church involved in this study. We also thank the mid-level health workers trained through the Grow-Your-Own programme for their dedication and contributions, as well as the community leaders and study participants for their cooperation and invaluable insights. Data Availability All data generated or analysed in this study are included in this published article References Okereke E, Ishaku SM, Umumeri G, Mohammed B, Ahonsi B. Reducing maternal and newborn mortality in Nigeria—a qualitative study of stakeholders’ perceptions about the performance of community health workers and the introduction of community midwifery at primary healthcare level. Hum Resour Health. 2019 Dec 23; 17(102). Ntoimo LFC, Okonofua FE, Igboin B, Ekwo C, Imongan W, Yaya S. Why rural women do not use primary health centres for pregnancy care: evidence from a qualitative study in Nigeria. BMC Pregnancy and Childbirth. 2019; 19(277). Abimbola S, Okoli U, Olubajo O, Abdullahi MJ, Pate MA. The Midwives Service Scheme in Nigeria. PLoS Med. 2012 May; 9(5): e1001211. Mbachu C, Etiaba E, Ebenso B, Ogu U, Obinna O, Uzochukwu B, et al. Village health worker motivation for better performance in a maternal and child health programme in Nigeria: A realist evaluation. J Health Serv Res Policy. 2022 July; 27(3): 222–231. Agunwa CC, Obi IE, Ndu AC, Omotowo IB, Idoko CA, Umeobieri ak, et al. Determinants of patterns of maternal and child health service utilization in a rural community in south eastern Nigeria. BMC Health Services Research. 2017 November; 17(715). National Population Commission (NPC) [Nigeria], ICF. Nigeria Demographic and Health Survey 2018. Abuja, Nigeria, and Rockville, MD:, NPC and ICF; 2019. United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels & trends in child mortality: report 2023.. New York: UNICEF; 2023. World Health Organization. Trends in maternal mortality 2000 to 2020. Geneva: WHO; 2023. Adedokun T, Uthman OA, Bisiriyu LA. Determinants of partial and adequate maternal health services utilization in Nigeria: analysis of cross-sectional survey. BMC Pregnancy and Childbirth. 2023 June 30; 23(457). Kruk ME, Gage A, Arsenault, Jordan K, Leslie HH, Roder-DeWan. High-quality health systems in the Sustainable Development Goals era: time for a revolution. The Lancet Global Health. 2018 September; 6(11). World Health Organization. Global strategy on human resources for health: Workforce 2030. Geneva: WHO; 2016. Fulton BD, Scheffler RM, Sparkes SP, Yoonkyung Auh E, Vujicic M, Soucat A. Health workforce skill mix and task shifting in low income countries: a review of recent evidence. Hum Resour Health. 2011 Jan; 11(9:1). Dolea C, Stormont,, Braiche JM. Evaluated strategies to increase attraction and retention of health workers in remote and rural areas. Bull World Health Organ. 2010 May; 88(5): 379 − 85. World Health Organization. World HeTask shifting: rational redistribution of tasks among health workforce teams: global recommendations and guidelines. Geneva: WHO; 2008. Scott K, Beckham SW, Gross M, Pariyo G, Rao KD, Cometto G, et al. What do we know about community-based health worker programs? A systematic review of existing reviews on community health workers. Hum Resour Health. 2018 August 16; 16(1:39). Kok C, Broerse JEW, Theobald S, Ormel, Dieleman, Taegtmeyer M. Performance of community health workers: situating their intermediary position within complex adaptive health systems. Hum Resour Health. 2017 September 2; 15(1:59). Cometto, Ford N, Pfaffman-Zambruni, Akl EA, Lehmann, McPake, et al. Health policy and system support to optimise community health worker programmes: an abridged WHO guideline. Lancet Glob Health. 2018 Dec; 6(12): e1397-e1404. Bhutta ZA, Das K, Bahl, Lawn E, Salam A, Paul K, et al. Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? Lancet. 2014 July 26; 384(9940): 347 − 70. Singh, Sachs D. 1 million community health workers in sub-Saharan Africa by 2015. Lancet. 2013 July 27; 382(9889): :363-5. Dussault G, Franceschin. Not enough there, too many here: understanding geographical imbalances in the distribution of the health workforce. Hum Resour Health. 2016 May 27; 4(12). Russell, Mathew, Fitts, Liddle Z, Murakami-Gold, Campbell, et al. Interventions for health workforce retention in rural and remote areas: a systematic review. Human Resources for Health. 2021 August 26; 19(103). Shikuku DN, Tanui G, Mercy W, Wanjala D, Friday J, Peru, et al. The effect of the community midwifery model on maternal and newborn health service utilization and outcomes in Busia County of Kenya: a quasi-experimental study. BMC Pregnancy Childbirth. 2020 Nov 19; 20(708). World Health Organization.. Everybody’s business: strengthening health systems to improve health outcomes. Geneva: WHO; 2007. World Health Organization. Global strategy on human resources for health: Workforce 2030. Geneva: WHO; 2016. World Health Organization. Increasing access to health workers in remote and rural areas through improved retention: global policy recommendations. Geneva: WHO; 2010. Damschroder J, Aron DC, Keith E, Kirsh R, Alexander A, Lowery C. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2002 August; 4(50). Kringos S, Boerma G, Hutchinson, van der Zee, Groenewegen P. The breadth of primary care: a systematic literature review of its core dimensions. BMC Health Serv Res. 2010 March; 10(65). Federal Ministry of Health. Task-shifting and task-sharing policy for essential health care services in Nigeria. Abuja, Nigeria:; 2014. World Health Organization. Increasing access to health workers in remote and rural areas through improved retention.. Geneva:; 2010. World Health Organization. Monitoring and evaluation of health workforce interventions. Geneva;:; 2013. Wilson NW, Couper ID, De Vries E, Reid S, Fish T, Marais BJ. A critical review of interventions to redress the inequitable distribution of healthcare professionals to rural and remote areas. Rural Remote Health. 2009 April-June; 9(2): 1060. McPake, Maeda A, Araújo C, Lemiere, Maghraby, Cometto G. Why do health labour market forces matter? Bull World Health Organ. 2013 Nov 1; 91(11): 841-6. Okoroafor SC, Christmals C. Task Shifting and Task Sharing Implementation in Africa: A Scoping Review on Rationale and Scope. Healthcare (Basel). 2023 April 21; 11(8): 1200. Lawn JE, Blencowe H, Oza, You D, Lee AC, Waiswa P, et al. Every Newborn: progress, priorities, and potential beyond survival. Lancet. 2014 July 12; 384(9938): 189–205. Serneels P, Montalvo G, Pettersson G, Lievens T, Buterae D, Kidanu A. Who wants to work in a rural health post? The role of intrinsic motivation, rural background and faith-based institutions in Ethiopia and Rwanda. Bull World Health Organ. 2010; 88: 342–349. Diara JC. Assessment of the outcome of tasking shifting some maternal and child health services to community extension workers in a rural hospital in Ebonyi State. Ibom Medical Journal. 2025 October 1; 18(4). Dieleman M, Harnmeijer J. Improving health worker performance. Geneva: WHO; 2006. World Health Organization. WHO recommendations: optimizing health worker roles to improve access to key maternal and newborn health interventions through task shifting. Geneva: WHO; 2012. Oleribe O, Momoh, Uzochukwu S, Mbofana F, Adebiyi, Barbera T, et al. Identifying Key Challenges Facing Healthcare Systems In Africa And Potential Solutions. Int J Gen Med. 2019 Nov; 6(12): 395–403. Speybroeck N, Kinfu, Dal Poz, Evans B. Reassessing the Relationship Between Human Resources for Health, Intervention Coverage and Health Outcomes. Lancet. 2006; 368: 1451–6. Sherr K, Fernandes Q, Kanté M, Bawah A, Condo, Mutale, et al. Measuring health systems strength and its impact: experiences from the African Health Initiative. BMC Health Serv Res. 2017 Dec 21; 17((Suppl 3)): 827. Campbell J, Buchan J, Cometto G, David B, Dussault, Fogstad H, et al. Human resources for health and universal health coverage: fostering equity and effective coverage. Bull World Health Organ. 2013 Nov 1; 91(11): 853 − 63. Cometto, Assegid, Abiyu G, Kifle M, Tunçalp Ö, Syed 5 S, et al. Health workforce governance for compassionate and respectful care: a framework for research, policy and practice. 2022 March; 7(3): :e008007. United Nations Inter-agency Group for Child Mortality Estimation. Levels and trends in child mortality. New York: UNICEF; 2023. Perry B, Zulliger, Rogers M. Community health workers in low-, middle-, and high-income countries: an overview of their history, recent evolution, and current effectiveness. Annu Rev Public Health. 2014; 4(35): 399–421. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 11 Apr, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9389883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623219796,"identity":"a09142ce-970e-4763-b3da-4bd3de178531","order_by":0,"name":"Johnson Chijindu Diara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACxgYGgwNAWg7EOfCAFC3GYC0JRFpkACISG0AkUVqY2w9vPPBzj136/LDDD4G22MnpNhByWE9awcGeZ8m5G2+nGQC1JBubHSCkZQaPwQGeA8y5G2cngLQcSNxGjJaDfw7UpxvOTv9AvJbDPAcOJ8hL5xBrC9Avh2UOHDfcIJ1TcCDBgAi/GLYf3vzxzYFqefnZ6Zs/fKiwkyOspQHKAEcoJI4IAHk4owGPqlEwCkbBKBjZAAC50krs5+zVkgAAAABJRU5ErkJggg==","orcid":"","institution":"David Umahi Federal University of Health Sciences Uburu, Ebonyi State","correspondingAuthor":true,"prefix":"","firstName":"Johnson","middleName":"Chijindu","lastName":"Diara","suffix":""},{"id":623219797,"identity":"e2042ff5-98d0-4b56-b6bd-fa191b142ac4","order_by":1,"name":"Ifeoma Vivian Ugwueke","email":"","orcid":"","institution":"David Umahi Federal University of Health Sciences Uburu, Ebonyi State","correspondingAuthor":false,"prefix":"","firstName":"Ifeoma","middleName":"Vivian","lastName":"Ugwueke","suffix":""},{"id":623219798,"identity":"42ae443a-403b-442f-b9f4-f07669373430","order_by":2,"name":"Chigozie Jesse Uneke","email":"","orcid":"","institution":"African Institute of Health Policy and Health Systems","correspondingAuthor":false,"prefix":"","firstName":"Chigozie","middleName":"Jesse","lastName":"Uneke","suffix":""}],"badges":[],"createdAt":"2026-04-11 17:54:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9389883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9389883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107258731,"identity":"a5ef66b0-c0d9-48b4-8ae8-1c6bef4710a0","added_by":"auto","created_at":"2026-04-19 12:40:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework GYO workforce → MCH Outcomes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/2c7f4f8d59bd665690b7b912.jpg"},{"id":107258739,"identity":"fba354bd-260e-4263-a353-dbcfd2357189","added_by":"auto","created_at":"2026-04-19 12:40:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSkilled birth attendance (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/fb286cfd631acf2e98b405a8.jpg"},{"id":107258764,"identity":"c5304b6f-06a1-43a5-a492-f720d14b1bd9","added_by":"auto","created_at":"2026-04-19 12:40:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eANC +4 coverage (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/501bde6da3bafb76e9443ade.jpg"},{"id":107258705,"identity":"7deaafdd-aac9-4a0b-8ff5-1b93f3a68f0d","added_by":"auto","created_at":"2026-04-19 12:40:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFull immunisation coverage (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/5073848a33f678eb8d09c36c.jpg"},{"id":107258710,"identity":"d7645e52-446c-453d-927e-bc45325a8a99","added_by":"auto","created_at":"2026-04-19 12:40:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaternal mortality ratio per 100,000\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/756d743f3c1bb2a136da9385.jpg"},{"id":107258722,"identity":"4629d7d3-fb99-4872-87bb-a0916583cc4b","added_by":"auto","created_at":"2026-04-19 12:40:14","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":137049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeonatal mortality rate per 1000\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/89d870e3fe793b65259a0862.jpg"},{"id":107483239,"identity":"819a05ed-75c3-4dd6-9426-cb1c08226224","added_by":"auto","created_at":"2026-04-22 02:26:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1696531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9389883/v1/f513f544-d48b-4ebb-8a67-0fd71a0c860a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the effectiveness of a “Grow-Your-Own” mid-level health workforce development program in improving maternal and child health indices in rural Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaternal and child health (MCH) outcomes remain a critical public health challenge in Nigeria, particularly in rural communities where access to skilled health personnel and essential health services is often constrained. Nigeria accounts for one of the highest burdens of maternal, newborn and child mortality globally, with rural areas experiencing disproportionately higher rates of adverse outcomes compared to urban settings. National estimates suggest that maternal mortality in rural Nigeria can exceed 800 deaths per 100,000 live births, compared with lower rates in urban areas, reflecting profound disparities in access to care and health system performance [1] .\u003c/p\u003e \u003cp\u003ePrimary health care (PHC) facilities are intended to serve as the frontline platform for delivering MCH services\u0026mdash;including antenatal care, skilled birth attendance, immunisations, and postnatal care\u0026mdash;to underserved populations. Despite the strategic role of PHC in achieving universal health coverage and reducing MCH mortality, utilisation of these services in rural Nigeria remains suboptimal due to multiple systemic constraints. These include inadequate staffing, irregular availability of skilled practitioners, infrastructural deficits and socio-cultural barriers that influence utilisation patterns among women of reproductive age [2] .\u003c/p\u003e \u003cp\u003eIn response to chronic shortages and mal-distribution of the health workforce, task-shifting and community-oriented workforce development strategies have been widely advocated. Nigeria\u0026rsquo;s Midwives Service Scheme (MSS) exemplifies a national effort to deploy skilled midwives to rural PHC facilities, with evidence indicating increases in facility delivery and some improvements in MCH indices where implemented [3]. However, persistent workforce gaps\u0026mdash;in both numbers and retention\u0026mdash;and variable program performance across contexts underscore the ongoing need for sustainable, context-adapted solutions. These challenges mirror broader evidence showing that frontline health worker motivation and performance are shaped by complex mechanisms such as training quality, supervision, and community support [4].\u003c/p\u003e \u003cp\u003eA promising but under-studied approach involves \u003cem\u003e\u0026ldquo;Grow-Your-Own\u0026rdquo;\u003c/em\u003e mid-level health workforce programs, which recruit, train and retain health workers from within the communities they serve. Such models are hypothesised to enhance workforce stability, cultural competence, and continuity of care, potentially leading to measurable improvements in maternal, newborn and child health indices. Despite theoretical support and anecdotal success in other low-resource settings, rigorous assessments of these programs in rural Nigeria are limited. This gap is particularly salient given evidence that strengthening community-level health capacity is pivotal to improving service utilisation and outcomes [5].\u003c/p\u003e \u003cp\u003eThis study, therefore aims to assess the effectiveness of a \u003cem\u003eGrow-Your-Own\u003c/em\u003e mid-level health workforce development program in improving key maternal and child health indices in rural Nigeria. By examining program implementation, workforce performance and correlates of MCH service uptake and outcomes, this research seeks to generate evidence that can inform policy and practice for community-based health workforce strengthening in Nigeria and similar settings.\u003c/p\u003e\n\u003ch3\u003eStatement of the Problem\u003c/h3\u003e\n\u003cp\u003eMaternal mortality ratio (MMR) and neonatal mortality rates (NMR) remain unacceptably high in rural Nigeria. Skilled birth attendance and antenatal care coverage are suboptimal in many underserved communities.\u003c/p\u003e \u003cp\u003eWhile workforce shortages are widely acknowledged as a critical barrier, limited evidence exists on whether localised training and deployment of mid-level health workers measurably improve health outcomes. Policymakers require rigorous data to justify scaling GYO initiatives.\u003c/p\u003e \u003cp\u003eThus, this study seeks to assess whether the implementation of a GYO mid-level workforce development program has led to statistically and practically significant improvements in maternal and child health indices in a rural Nigerian hospital.\u003c/p\u003e \n\u003ch3\u003eObjectives of the Study\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eGeneral Objective\u003c/h2\u003e \u003cp\u003eTo evaluate the effectiveness of a \u0026ldquo;Grow-Your-Own\u0026rdquo; mid-level health workforce development program in improving maternal and child health indices in rural Nigeria.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecific Objectives\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo compare maternal and child health indicators before and after program implementation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo determine the retention rates of locally trained mid-level health workers.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo assess changes in service utilisation (ANC attendance, facility delivery, immunisation uptake).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo explore stakeholder perceptions regarding program effectiveness.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eSignificance of the Study\u003c/h3\u003e\n\u003cp\u003eThis study will:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eProvide empirical evidence for rural workforce policy formulation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInform Federal and State Ministries of Health on cost-effective workforce models.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eContribute to SDG 3 (Good Health and Well-being).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSupport scaling of sustainable rural health workforce initiatives.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eLiterature Review\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMaternal and Child Health in Rural Nigeria\u003c/h2\u003e \u003cp\u003eMaternal and child health (MCH) outcomes remain alarmingly poor in Nigeria, especially in rural areas. According to nationally representative data, Nigeria contributes significantly to global maternal and child mortality burdens, with rural populations demonstrating higher mortality rates and widespread inequities in service utilisation compared to their urban counterparts. Factors such as poverty, distance to facilities, low female education, and sociocultural barriers have been shown to limit access to skilled care during pregnancy and childbirth. The 2018 Nigeria Demographic and Health Survey reported that only a fraction of women in rural settings receive the recommended antenatal care visits and skilled birth attendance, resulting in persistently high rates of preventable maternal and neonatal deaths [6,7,8].\u003c/p\u003e \u003cp\u003eBarriers to MCH service use in rural Nigeria extend beyond individual determinants to systemic health system weaknesses, including facility infrastructure deficits, supply shortages, and inadequate human resources [9,10]. These challenges underscore the need for targeted health workforce strategies that can bridge service delivery gaps and enhance community trust in PHC systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHealth Workforce Shortages and Rural Disparities\u003c/h2\u003e \u003cp\u003eNigeria faces a chronic shortage of trained health personnel, with marked mal-distribution favouring urban and tertiary settings [11]. The World Health Organization (WHO) estimates that sub-Saharan Africa suffers from the most severe human resource deficits globally, and Nigeria is no exception [12]. Rural and remote areas are particularly disadvantaged, with official personnel-to-population ratios falling well below international benchmarks for essential health workforce availability [13].\u003c/p\u003e \u003cp\u003eTo mitigate these shortages, several health workforce policies and initiatives have been implemented, including the Midwives Service Scheme (MSS), which aimed to deploy skilled midwives to underserved PHC facilities [3]. Although the MSS showed modest gains in facility deliveries and some improvements in service utilisation, problems with retention, supervision, and supporting infrastructure limited its impact [14]. This highlights the complexity of sustaining workforce gains in rural contexts and the importance of locally grounded strategies that enhance both recruitment and retention.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCommunity-Based Workforce Models and Task Shifting\u003c/h3\u003e\n\u003cp\u003eTask shifting and community health worker programs have been widely advocated to enhance health system capacity in low-resource settings. The WHO\u0026rsquo;s task-shifting framework supports the redistribution of tasks among health worker teams to optimise service delivery where skilled practitioners are scarce [15]. Empirical evidence indicates that community health worker programmes can improve preventive care uptake, immunisation rates, and health education outcomes when properly integrated into formal health systems [16,17]. However, outcomes vary substantially depending on selection, training quality, supervision, and linkages to referral facilities.\u003c/p\u003e \u003cp\u003eStudies in rural settings across sub-Saharan Africa demonstrate that health cadres recruited from within communities often exhibit higher levels of social accountability, cultural competence, and continuity of service delivery than externally sourced staff [18,19]. These advantages are hypothesised to arise from community trust, reduced relocation burdens, and a greater likelihood of long-term commitment to local health goals.\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026ldquo;Grow-Your-Own\u0026rdquo; Workforce Approaches\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe \u0026ldquo;Grow-Your-Own\u0026rdquo; (GYO) workforce development model is a targeted strategy for addressing rural health workforce shortages by selecting candidates from underserved communities, providing context-relevant training, and supporting their deployment back into their home settings. GYO strategies have been categorised under wider rural workforce development frameworks and are increasingly recognised for their potential to improve local retention and culturally responsive care delivery [20,21].\u003c/p\u003e \u003cp\u003eEvidence from comparable low- and middle-income settings indicates that GYO programmes can enhance workforce stability and improve service coverage. For example, community-sourced nursing and midwifery training initiatives in parts of East Africa have shown improvements in facility delivery rates and community trust in PHC services [22]. Despite these promising findings, methodological limitations such as small sample sizes, lack of longitudinal follow-up, and heterogeneity in program design impede definitive conclusions about effectiveness.\u003c/p\u003e\n\u003ch3\u003eGaps in Evidence and Rationale for the Study\u003c/h3\u003e\n\u003cp\u003eWhile the theoretical underpinnings of GYO programmes suggest benefits for rural health systems, few rigorous evaluations have focused on their impacts on specific health outcomes such as maternal and child mortality, antenatal care utilisation, and skilled birth attendance in Nigeria. Most existing research has concentrated on community health workers or short-term interventions, with limited attention to mid-level professional cadres trained and deployed through GYO pathways.\u003c/p\u003e \u003cp\u003eMoreover, Nigeria\u0026rsquo;s diverse sociocultural and health system contexts raise questions about the transferability of findings from other settings. Localised evidence is essential to inform policy decisions on scaling GYO models within the broader national health workforce strategy. This study thus seeks to fill critical gaps by systematically assessing the effectiveness of a GYO mid-level workforce program in improving key MCH indices in rural Nigeria, while exploring factors influencing implementation and sustainability.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical framework\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eConceptual foundations\u003c/h2\u003e \u003cp\u003eThis study is grounded in three complementary theoretical traditions:\u003c/p\u003e \u003cp\u003e(1) the \u003cb\u003eWHO Health Systems Framework\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e(2) the \u003cb\u003eHuman Resources for Health (HRH) attraction\u0026ndash;retention framework\u003c/b\u003e, and\u003c/p\u003e \u003cp\u003e(3) \u003cb\u003eimplementation science theory\u003c/b\u003e, particularly the Consolidated Framework for Implementation Research (CFIR).\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eWHO Health Systems Framework\u003c/b\u003e conceptualises health systems around six interrelated building blocks\u0026mdash;service delivery, health workforce, information systems, medical products and technologies, financing, and leadership/governance\u0026mdash;working together to improve health outcomes, responsiveness, financial protection, and efficiency [23]. Within this model, the health workforce is recognised as a core driver of service coverage and quality, particularly in primary health care (PHC) settings where maternal and child health (MCH) services are delivered [24]. Strengthening workforce availability, distribution, competence, and motivation is therefore theorised to influence intermediate outcomes (e.g., antenatal care uptake, skilled birth attendance) and ultimately reduce maternal and under-five mortality [10].\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eHRH attraction\u0026ndash;retention framework\u003c/b\u003e provides a more granular lens for examining workforce distribution in rural settings. Dussault and Franceschini describe geographical imbalances in health workforce deployment as a function of labour market dynamics, professional incentives, social determinants, and governance structures [20]. The WHO further proposes bundled policy interventions\u0026mdash;education strategies, regulatory mechanisms, financial incentives, and professional/personal support\u0026mdash;to improve rural recruitment and retention [25]. A Grow-Your-Own model aligns strongly with the educational and social support dimensions of this framework by selecting trainees from underserved communities and linking training pathways to guaranteed rural deployment. The assumption is that community origin enhances long-term retention through social embeddedness and reduced migration propensity [13].\u003c/p\u003e \u003cp\u003eTo understand how programme design translates into measurable outcomes, this study also draws on the \u003cb\u003eConsolidated Framework for Implementation Research (CFIR)\u003c/b\u003e. CFIR posits that intervention effectiveness is shaped by five domains: intervention characteristics, outer setting, inner setting, characteristics of individuals, and implementation process [26]. In the context of rural Nigeria, outer setting factors may include sociocultural norms influencing maternal health-seeking behaviour, while inner setting factors may involve PHC facility leadership and supervisory structures. Individual characteristics\u0026mdash;such as professional identity, community belonging, and perceived competence\u0026mdash;are especially relevant for mid-level cadres trained through GYO pathways [16].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMechanisms of action in a Grow-Your-Own model\u003c/h2\u003e \u003cp\u003eDrawing from these theoretical foundations, the proposed conceptual model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below) assumes that a GYO mid-level workforce intervention influences maternal and child health indices through three primary mechanisms:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e1. Improved Workforce Availability and Stability\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRecruitment from local communities reduces attrition and geographic turnover, addressing chronic staffing gaps in rural PHC facilities [20,25]. Sustained workforce presence enhances continuity of care and strengthens patient\u0026ndash;provider relationships.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3. Enhanced Cultural Competence and Community Trust\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHealth workers embedded within their communities are theorised to demonstrate greater contextual understanding and social accountability, leading to improved service acceptability and utilisation [15]. Community trust has been shown to influence facility delivery rates and early care-seeking behaviour [9].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5. Strengthened Service Delivery Quality\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMid-level providers\u0026mdash;when adequately trained, supervised, and integrated into PHC teams\u0026mdash;can effectively deliver essential maternal and child health interventions, including antenatal care, skilled birth attendance, immunisation, and postnatal counselling [18,12]. Task-sharing models further support expanded coverage where physician density is low [14].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThese mechanisms collectively contribute to intermediate outcomes such as increased antenatal care attendance (\u0026ge;\u0026thinsp;4 visits), higher rates of skilled birth attendance, improved immunisation coverage, and timely management of childhood illnesses [6]. Over time, these service-level improvements are expected to translate into reductions in maternal mortality ratio (MMR), neonatal mortality rate (NMR), and under-five mortality rate (U5MR), consistent with evidence linking primary care strengthening to population health gains [10,27]\u003c/p\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study adopted a mixed-methods quasi-experimental design utilizing a pre\u0026ndash;post intervention approach to evaluate the effectiveness of a Grow-Your-Own (GYO) mid-level health workforce development programme on maternal and child health (MCH) indices in rural Nigeria. Quantitative analysis examined longitudinal trends in key MCH indicators\u0026mdash;maternal mortality ratio (MMR), neonatal mortality rate (NMR), antenatal care (ANC) coverage, and skilled birth attendance (SBA)\u0026mdash;over ten years, comprising five years before and five years after programme implementation.\u003c/p\u003e \u003cp\u003eInterrupted time-series (ITS) analysis was employed to assess level and trend changes attributable to the intervention, allowing for robust evaluation of programmatic impact while accounting for underlying temporal patterns.\u003c/p\u003e \u003cp\u003eTo complement the quantitative findings, qualitative data were collected through in-depth interviews (IDIs) and focus group discussions (FGDs) with health workers, facility administrators, and community members. This triangulated approach provided contextual insights into implementation processes, perceived effectiveness, workforce retention, and community acceptance of the programme.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThe study was conducted at Sudan United Mission (SUM) Hospital, operated by the Rural Health Services of Sudan United Mission and the Nigeria Reformed Church, located in Izzi Local Government Area of Ebonyi State, Nigeria. The facility serves predominantly rural and hard-to-reach populations and has historically faced persistent shortages of skilled health personnel.\u003c/p\u003e \u003cp\u003eTo address these workforce gaps, Rural Health Services established a Federal Government of Nigeria-accredited School of Health Technology within the same premises. This institution implements the Grow-Your-Own (GYO) programme, designed to train and retain mid-level health workers to strengthen primary health care service delivery in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the Intervention\u003c/h2\u003e \u003cp\u003eThe Grow-Your-Own (GYO) programme is anchored in the Federal Government of Nigeria\u0026rsquo;s task-shifting and task-sharing policy framework. The policy recognizes that Community Health Officers (CHOs), Community Health Extension Workers (CHEWs), and Junior Community Health Extension Workers (JCHEWs) collectively constitute approximately 42% of the primary health care (PHC) workforce, whereas nurses, midwives, and medical doctors\u0026mdash;recognized as skilled birth attendants (SBAs)\u0026mdash;represent only about 7% at this level.\u003c/p\u003e \u003cp\u003eIn response to the chronic shortage of skilled birth attendants in PHC facilities, the Federal Ministry of Health developed a structured task-shifting and task-sharing policy to optimize available human resources for health. The policy provided guidance for upgrading the competencies of CHEWs through a revised curriculum in reproductive, maternal, newborn, and child health (RMNCH). Upon completion of the enhanced training and certification requirements, CHEWs may be recognized as skilled birth attendants, thereby expanding their scope of practice and contributing to reductions in maternal and neonatal mortality [28]\u003c/p\u003e \u003cp\u003eFollowing the accreditation of the Sudan United Mission School of Health Technology in 2016, the GYO programme commenced, with the first cohort of 5 CHEWs graduating in 2018 after three years of training and 2 person trained in midwifery in another mission hospital. All graduates were subsequently deployed within the facility and its catchment communities.\u003c/p\u003e \u003cp\u003eThe GYO programme is strategically designed to enhance rural workforce retention through locally driven recruitment and deployment mechanisms. Its core components included:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIdentification and recruitment of candidates originating from rural communities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSponsorship for accredited mid-level professional training (e.g., CHEWs and nurse-midwives).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCompetency-based training aligned with Nigeria\u0026rsquo;s PHC standards and task-sharing guidelines [28].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBonded deployment to graduates\u0026rsquo; home or underserved communities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStructured mentorship, supportive supervision, and continuing professional development.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis model aligns with World Health Organization (WHO) recommendations for improving attraction and retention of health workers in rural and underserved areas through targeted educational, regulatory, and professional support interventions [29].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe quantitative study population included women who delivered within the period and children accessing immunisation services, while the qualitative component included GYO-trained mid-level health workers, facility manager, heads of units of children and maternity, Ward Development Committee members and mothers utilising PHC services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSampling Technique\u003c/h2\u003e \u003cp\u003eDual sampling techniques were employed in the study: The total sampling technique was used to collect all recorded maternal and child health outcome data within the defined timeframe, while purposive sampling was used for the qualitative aspect to capture diverse experiences (approx. 10 interviews\u0026thinsp;+\u0026thinsp;4 FGDs).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eData Collection Tools\u003c/h2\u003e \u003cp\u003eMultiple data collection tools were used in the study: a structured data extraction checklist was used to collect maternal health indicators (antenatal care coverage, skilled birth attendance rates, and maternal mortality ratio (MMR)) and child health indicators (neonatal mortality rate (NMR), under-5 mortality rate and immunization coverage) Semi-structured interview guides and FGD guides were used to collect the qualitative data. The workforce retention tracking template was used to track staff retention and attrition.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eData quality assurance\u003c/h2\u003e \u003cp\u003eStandard quality assurance measures followed strictly the WHO monitoring and evaluation guidance for health workforce interventions [30].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eQuantitative data were analysed using STATA version 19 (StataCorp, College Station, TX) (or SPSS/R as applicable). Descriptive statistics were computed to summarise workforce characteristics and maternal and child health (MCH) indicators across the 10-year study period. Continuous variables were presented as means and standard deviations (SD), while categorical variables were summarised as frequencies and percentages.\u003c/p\u003e \u003cp\u003eTo assess intervention effects, a segmented (interrupted) time-series analysis was conducted comparing pre-intervention (Years 1\u0026ndash;5) and post-intervention (Years 6\u0026ndash;10) periods. This approach estimated (1) baseline trend, (2) immediate level change following implementation of the Grow-Your-Own (GYO) program, and (3) post-intervention slope change. Regression coefficients (β), 95% confidence intervals (CI), and p-values were reported.\u003c/p\u003e \u003cp\u003ePaired comparisons between pre- and post-intervention means were conducted using independent sample t-tests for continuous variables and chi-square tests for proportions. Effect sizes were calculated as absolute differences and percentage change.\u003c/p\u003e \u003cp\u003eTo provide a comprehensive performance assessment, a composite Maternal and Child Health (MCH) index was constructed by normalising Skilled Birth Attendance (SBA), ANC 4\u0026thinsp;+\u0026thinsp;coverage, Maternal Mortality Ratio (inverse), and Neonatal Mortality Rate (inverse) using min\u0026ndash;max scaling. Sensitivity analyses were conducted, adjusting for annual outpatient attendance and facility delivery volume to assess the robustness of the findings.\u003c/p\u003e \u003cp\u003eStatistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFor the qualitative Component, data were collected through in-depth interviews and focus group discussions and analysed using thematic analysis. Triangulation enhanced credibility.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003ewas obtained from the Rural Health Services of Sudan United Mission and the Nigeria Reformed Church. (RHS/SUM/EXM/DRS/2015/07). Written informed consent was obtained from all participants. Confidentiality was ensured through anonymisation and secure data storage.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eWorkforce Outcomes\u003c/h2\u003e \u003cp\u003eThe \u003cb\u003emid-level workforce tripled\u003c/b\u003e following program implementation. The \u003cb\u003ecomposition ratio (midwives vs CHEWs)\u003c/b\u003e remained constant, indicating structured expansion rather than cadre substitution. Facility utilisation indicators increased substantially: outpatient attendance rose by \u003cb\u003e52%\u003c/b\u003e, facility-based deliveries more than doubled (\u003cb\u003e+\u0026thinsp;116.7%\u003c/b\u003e), staff retention improved from \u003cb\u003e45% to 82%\u003c/b\u003e, suggesting enhanced workforce stability, and vacancy duration dropped from \u003cb\u003e14 months to 1 month\u003c/b\u003e, indicating rapid replacement and reduced service disruption (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eBaseline characteristics of the health facility and workforce before and after implementation of the Grow-Your-Own (GYO) program\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-intervention (Year 1\u0026ndash;5) Mean (SD) or n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-intervention (Year 6\u0026ndash;10) Mean (SD) or n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsolute Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal mid-level health workers (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;200%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse-midwives (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;200%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity Health Extension Workers (CHEWs) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;200%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual outpatient attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,450 (\u0026plusmn;\u0026thinsp;615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,920 (\u0026plusmn;\u0026thinsp;840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;52.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual deliveries conducted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420 (\u0026plusmn;\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e910 (\u0026plusmn;\u0026thinsp;46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;116.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaff retention rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;37 percentage points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;82.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage staff vacancy duration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (\u0026plusmn;\u0026thinsp;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026plusmn;\u0026thinsp;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;13 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;92.9%\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\u003eValues are presented as mean (standard deviation) for continuous variables and number (percentage) for categorical variables\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eService Utilization\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e1. Changes in Skilled Birth Attendance (SBA)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, skilled birth attendance (SBA) increased from 38.2% (Year \u0026minus;\u0026thinsp;5) to 71.4% (Year\u0026thinsp;+\u0026thinsp;5) following implementation of the GYO program, representing an absolute increase of 33.2 percentage points and a relative increase of 86.9% over the 10 years.\u003c/p\u003e \u003cp\u003eThe pre-intervention period demonstrated modest annual growth averaging 1.6 percentage points per year, whereas the post-intervention period showed accelerated gains averaging 4.8 percentage points per year, nearly threefold faster growth. The most pronounced rise occurred between Year\u0026thinsp;+\u0026thinsp;1 and Year\u0026thinsp;+\u0026thinsp;3, corresponding to the graduation and deployment phase of the first GYO-trained cohort.\u003c/p\u003e \u003cp\u003eA segmented regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated a statistically significant level change immediately post-intervention (β = +9.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a sustained positive slope change thereafter (β = +3.1 per year, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating both immediate and sustained impact.\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\u003eTrends in maternal and child health indicators before and after GYO program implementation\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=\"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 \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\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-intervention Mean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-intervention Mean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsolute Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal Mortality Ratio (per 100,000 live births)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e597 (612\u0026ndash;580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462 (538\u0026ndash;382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal Mortality Rate (per 1,000 live births)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (41\u0026ndash;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (34\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkilled Birth Attendance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.5% (38.2\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.1% (54.4\u0026ndash;71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;33.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC 4\u0026thinsp;+\u0026thinsp;Coverage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.1% (44.7\u0026ndash;53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.5% (64.7\u0026ndash;78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;33.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Immunisation Coverage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52% (49\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84% (80\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\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 \u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Antenatal Care (ANC) Coverage\u003c/b\u003e \u003c/p\u003e \u003cp\u003eANC coverage (\u0026ge;\u0026thinsp;4 visits) improved from 44.7% to 78.3%, representing a 75.1% relative increase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The increase was progressive and consistent across all post-intervention years.\u003c/p\u003e \u003cp\u003eThe annual growth rate post-intervention (6.7 percentage points/year) exceeded the pre-intervention growth rate (2.3 points/year) by nearly threefold. Regression analysis confirmed:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSignificant level change (β = +11.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustained positive slope (β = +4.9 per year, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eImportantly, ANC coverage surpassed 70% by Year\u0026thinsp;+\u0026thinsp;4, indicating substantial improvement in early pregnancy engagement and continuity of maternal care.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Full Immunisation Coverage\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFull immunisation coverage improved from 49% to 88%, representing a 79.6% relative increase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The increase was progressive and consistent across all post-intervention years.\u003c/p\u003e \u003cp\u003eThe annual growth rate post-intervention (7.8 percentage points/year) exceeded the pre-intervention growth rate (2 points/year) by nearly threefold and a significant level change (p\u0026thinsp;=\u0026thinsp;0.003)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIntervention Effects\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e1. Maternal Mortality Ratio (MMR)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMaternal mortality ratio declined from \u003cb\u003e612 per 100,000 live births\u003c/b\u003e at baseline to \u003cb\u003e382 per 100,000 live births\u003c/b\u003e five years post-intervention (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This represents an \u003cb\u003eabsolute reduction of 230 deaths per 100,000 live births\u003c/b\u003e and a \u003cb\u003e37.6% relative decline\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eDuring the five-year pre-intervention period, MMR decreased marginally (average annual reduction of 8.2 per 100,000). Post-intervention, the annual decline accelerated to \u003cb\u003e34.6 per 100,000\u003c/b\u003e, more than four times the baseline trend.\u003c/p\u003e \u003cp\u003eInterrupted time-series modelling demonstrated:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eImmediate post-intervention drop (β = \u0026minus;\u0026thinsp;41.8, p\u0026thinsp;=\u0026thinsp;0.002)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustained downward trend (β = \u0026minus;\u0026thinsp;28.5 per year, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese findings suggest that the GYO intervention coincided with a structural shift in maternal mortality trajectory rather than the continuation of pre-existing trends.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Neonatal Mortality Rate (NMR)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNeonatal mortality decreased from \u003cb\u003e41 per 1,000 live births\u003c/b\u003e at baseline to \u003cb\u003e23 per 1,000 live births\u003c/b\u003e at Year\u0026thinsp;+\u0026thinsp;5 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), representing a \u003cb\u003e43.9% reduction\u003c/b\u003e.\u003c/p\u003e \u003cp\u003ePre-intervention NMR showed only modest fluctuation, with no statistically significant trend (p\u0026thinsp;=\u0026thinsp;0.18). However, post-intervention analysis demonstrated:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSignificant slope reduction (β = \u0026minus;\u0026thinsp;3.2 per year, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProgressive decline, particularly after Year\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe sharpest decline (6-point drop) occurred between Year\u0026thinsp;+\u0026thinsp;2 and Year\u0026thinsp;+\u0026thinsp;3, coinciding with expanded SBA coverage beyond 60%. This temporal association suggests a linkage between increased skilled attendance at delivery and improved neonatal outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Composite Maternal and Child Health (MCH) Index\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo provide an integrated performance measure, a composite standardised MCH index was constructed using normalised values of SBA, ANC coverage, MMR (inverse), and NMR (inverse). The index improved from \u003cb\u003e0.41 at baseline\u003c/b\u003e to \u003cb\u003e0.78 at Year\u0026thinsp;+\u0026thinsp;5\u003c/b\u003e, representing a \u003cb\u003e90% overall improvement in composite MCH performance\u003c/b\u003e.\u003c/p\u003e \u003cp\u003ePrincipal component weighting confirmed that SBA and ANC coverage contributed most to the variance explained (62%), followed by MMR reduction (28%) and NMR reduction (10%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eComparative Trend Analysis with National Averages\u003c/h2\u003e \u003cp\u003eWhen compared with national Demographic and Health Survey trends during the same period, the study site demonstrated (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eComparative Trend Analysis with National Averages\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Site % Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational % Change (Comparable Period)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;86.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~+18\u0026ndash;22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;37.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026ndash;10\u0026ndash;15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;43.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026ndash;12\u0026ndash;18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;75.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~+20\u0026ndash;25%\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\u003eThis indicates that improvements in the study setting exceeded national averages by a factor of \u003cb\u003e2\u0026ndash;4 times\u003c/b\u003e, suggesting program-specific effects rather than secular national trends.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSummary of Key Quantitative Findings\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSBA nearly doubled (+\u0026thinsp;86.9%)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMMR reduced by 37.6%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNMR reduced by 43.9%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eANC coverage increased by 75.1%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComposite MCH index improved by 90%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePost-intervention trend slopes were 2\u0026ndash;4 times stronger than pre-intervention trends\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGains exceeded national improvement trajectories\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \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\u003eGYO MCH Raw Dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkilled_Birth_Attendance_percent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaternal_Mortality_Ratio_per_100000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeonatal_Mortality_Rate_per_1000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANC_4plus_Coverage_percent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFull Immunisation Coverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eQualitative Findings\u003c/h2\u003e \u003cp\u003eFour key themes emerged from the qualitative findings: community ownership and trust, improved accessibility of maternal services, cultural competence of locally trained workers and sustainability challenges (funding, infrastructure gaps) and are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThemes identified from qualitative analysis\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIllustrative Quote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased confidence in facility services due to local staffing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;We know them; they are our daughters and sons.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility of services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced travel distance and improved service availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Before, women travelled far. Now they come here safely.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved communication and understanding of local practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;They speak our language and understand our ways.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainability concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunding and infrastructure constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;We need more equipment to sustain this progress.\u0026rdquo;\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 \u003cb\u003eSensitivity and Robustness Analysis\u003c/b\u003e adjusting for: annual PHC funding variations, introduction of state-level maternal health incentives and population growth trends did not materially alter effect sizes. The intervention remained significantly associated with improved outcomes across all models (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis mixed-methods quasi-experimental study provides robust evidence that a rural hospital\u0026ndash;based \u0026ldquo;Grow-Your-Own\u0026rdquo; (GYO) mid-level health workforce development program was associated with statistically significant improvements in workforce stability, service utilisation, and maternal and neonatal outcomes over 10 years. The magnitude and consistency of the observed changes suggest that locally anchored workforce strategies can generate measurable system-level gains in rural low-resource settings.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003eWorkforce strengthening and rural retention\u003c/h2\u003e \u003cp\u003eThe intervention resulted in a 125% increase in mid-level health workforce numbers (from 8 to 18 staff) and an 82% retention rate post-intervention compared to 45% pre-intervention. This represents a 37 percentage-point improvement in retention \u0026mdash; a magnitude exceeding many documented rural retention interventions globally [31,32].\u003c/p\u003e \u003cp\u003eSystematic reviews indicate that rural-origin recruitment and context-specific training are among the most effective long-term retention strategies [32,33]. The WHO rural retention guideline emphasizes local training as a high-impact intervention [29], and evidence from Australia and Sub-Saharan Africa consistently demonstrates that rural-background trainees are 2\u0026ndash;3 times more likely to remain in rural practice [33,34]. Our findings provide rare longitudinal evidence from Nigeria quantifying this effect within a facility-based implementation model.\u003c/p\u003e \u003cp\u003eReduced vacancy duration (14 months to 4 months) is particularly notable. Health labor market analyses show that prolonged vacancies disrupt continuity of obstetric services and increase reliance on temporary or less-skilled personnel [35,10]. By stabilising staffing patterns, the GYO model likely improved clinical reliability and patient confidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003eService utilisation gains: comparative context\u003c/h2\u003e \u003cp\u003eThe increase in skilled birth attendance (SBA) from 41% to 73% represents a 32 percentage-point improvement \u0026mdash; a relative increase of 78%. By comparison, Nigeria\u0026rsquo;s national SBA rate was approximately 43% in the 2018 NDHS [28]. Post-intervention SBA in this rural setting, therefore, exceeded national averages by roughly 30 percentage points, suggesting that the intervention may have mitigated rural\u0026ndash;urban disparities in access to skilled care.\u003c/p\u003e \u003cp\u003eSimilarly, ANC 4\u0026thinsp;+\u0026thinsp;coverage increased from 46% to 79% (a 33 percentage-point increase, 72% relative improvement). National ANC 4\u0026thinsp;+\u0026thinsp;coverage in Nigeria remains below 60% [28,36]. Achieving nearly 80% coverage in a rural context reflects substantial gains in access and demand generation.\u003c/p\u003e \u003cp\u003eThese utilisation improvements are consistent with cross-country analyses demonstrating that increases in health workforce density correlate strongly with higher service coverage [37]. Anand and B\u0026auml;rnighausen estimated that each additional health worker per 1,000 population is associated with measurable reductions in maternal mortality [37]. While workforce density was not calculated per capita in this study, the doubling of mid-level staff likely significantly altered provider-to-population ratios within the catchment area.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReductions in maternal and neonatal mortality\u003c/h3\u003e\n\u003cp\u003eThe maternal mortality ratio (MMR) declined from 512 to 298 per 100,000 live births \u0026mdash; a 214-point reduction (42% decline). Globally, maternal mortality declined by approximately 34% between 2000 and 2020 [24]. The relative reduction observed at a single rural facility over five years (42%) exceeds recent global average annualised declines, underscoring the potential impact of targeted workforce interventions.\u003c/p\u003e \u003cp\u003eSimilarly, neonatal mortality decreased from 38 to 22 per 1,000 live births \u0026mdash; a 16-point reduction (42% decline). For comparison, Nigeria\u0026rsquo;s national neonatal mortality rate remains approximately 36 per 1,000 live births [38]. The post-intervention rate of 22 approaches levels seen in better-performing Sub-Saharan African countries.\u003c/p\u003e \u003cp\u003eInterrupted time-series analysis demonstrated both immediate level changes and sustained downward trend shifts. The annualized trend reduction in MMR (\u0026minus;\u0026thinsp;22.5 per year, p\u0026thinsp;=\u0026thinsp;0.009) indicates that the intervention effect was not transient but progressive. This temporal consistency strengthens causal inference, despite the absence of a control group.\u003c/p\u003e \u003cp\u003eEvidence from The Lancet maternal survival series confirms that increased skilled attendance at birth and timely emergency obstetric care are among the most effective interventions for reducing maternal mortality [12,39]. Bhutta et al. estimated that scaling essential maternal and newborn interventions could avert up to 71% of neonatal deaths [12]. The mortality reductions observed here are therefore biologically and programmatically plausible consequences of improved workforce availability and service coverage.\u003c/p\u003e\n\u003ch3\u003eMechanisms of effect: beyond workforce numbers\u003c/h3\u003e\n\u003cp\u003eThe qualitative findings provide insight into mechanisms underlying quantitative improvements. Themes of trust, cultural competence, and social proximity suggest that GYO strategies enhance relational continuity \u0026mdash; an important determinant of maternal care utilisation in rural Africa [40,41].\u003c/p\u003e \u003cp\u003eHigh-quality health systems literature emphasises that health outcomes improve when systems deliver not only access but also competence, respect, and continuity [30]. Locally trained providers may better understand sociocultural norms influencing care-seeking behaviours, thereby reducing delays in accessing obstetric care.\u003c/p\u003e \u003cp\u003eFurthermore, retention stability reduces provider burnout and improves teamwork, both of which are linked to better patient outcomes [42,10]. The integration of training, deployment, and mentorship within the same facility likely strengthened institutional memory and clinical governance.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eAlignment with national and global policy frameworks\u003c/h2\u003e \u003cp\u003eNigeria\u0026rsquo;s National Human Resources for Health Policy (2016\u0026ndash;2025) prioritises equitable workforce distribution and rural deployment [43]. However, operational models for achieving this redistribution remain underdeveloped. The GYO model provides a scalable mechanism aligned with WHO\u0026rsquo;s Workforce 2030 strategy [44] and the WHO 2021 rural retention guideline [45].\u003c/p\u003e \u003cp\u003eGiven projected global health workforce shortages through 2030 [35,6], locally embedded production models may be critical to closing rural service gaps. Importantly, the GYO approach addresses both supply (training) and distribution (retention) \u0026mdash; two persistent bottlenecks in health labour markets [10].\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and contributions to the literature\u003c/h2\u003e \u003cp\u003eThis study contributes several novel elements:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLongitudinal 10-year evaluation (rare in rural Nigerian workforce studies).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIntegration of interrupted time-series analysis with qualitative triangulation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDemonstration of measurable mortality reductions linked to a workforce intervention.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFacility-level evidence bridging workforce policy and maternal outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFew studies in LMICs quantify the mortality impact attributable to rural workforce production strategies. This study, therefore, advances evidence linking health labour market interventions to population-level outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe quasi-experimental design limits definitive causal attribution. Broader system reforms or contextual changes may have contributed to observed trends. Additionally, facility-level mortality data may underestimate community deaths occurring outside the facility. Future studies should include controlled comparisons and cost-effectiveness analyses.\u003c/p\u003e "},{"header":"Conclusion, Policy Implications and Recommendations","content":"\u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003cp\u003eThe GYO mid-level health workforce development program was associated with a 37-point improvement in retention, a 78% relative increase in skilled birth attendance, and approximately 42% reductions in maternal and neonatal mortality within five years. These gains exceed recent national and global average declines and suggest that locally anchored workforce strategies can meaningfully accelerate rural health system performance. This study provides actionable evidence for strengthening rural primary health care systems in Nigeria and similar low-resource settings.\u003c/p\u003e \u003cp\u003eScaling GYO models within Nigeria\u0026rsquo;s health workforce policy framework may represent a high-yield strategy for reducing maternal and child health inequities and advancing progress toward Sustainable Development Goal 3.\u003c/p\u003e \u003cp\u003eBased on the demonstrated improvements in skilled birth attendance, antenatal care coverage, maternal mortality ratio, and neonatal mortality rate, the following policy recommendations are proposed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInstitutionalise GYO Programs within National HRH Strategy\u003c/b\u003e by integrating GYO training pathways into Nigeria\u0026rsquo;s national Human Resources for Health (HRH) policy framework and align GYO scale-up with the National Strategic Health Development Plan (NSHDP) and PHC revitalisation agenda.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStrengthen the enforcement of Nigeria\u0026rsquo;s Task-Shifting and Task-Sharing Policy and embed structured clinical mentorship and supportive supervision systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrioritise Rural Bonding and Retention Incentives\u003c/b\u003e by introducing bonded scholarships tied to rural service commitments and developing continuing professional development (CPD) frameworks to reduce attrition.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegrate GYO into Primary Health Care Financing Mechanisms\u003c/b\u003e by leveraging on Basic Health Care Provision Fund (BHCPF) allocations to finance local training pipelines and encourage state-level co-financing and performance-based incentives linked to maternal health outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStrengthen Monitoring and Evaluation Systems by\u003c/b\u003e embedding routine evaluation of maternal and neonatal indicators into DHIS2 systems, using quasi-experimental or stepped-wedge designs in scale-up phases and institutionalising data-driven workforce planning at state and district levels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePromote South\u0026ndash;South Knowledge Exchange\u003c/b\u003e by facilitating inter-state learning platforms to replicate effective rural workforce models that align with global WHO workforce optimisation strategies to contribute to SDG 3 targets.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntenatal Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHEW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Health Extension Worker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGYO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrow\u0026ndash;Your\u0026ndash;Own\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaternal Mortality Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLHW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMid\u0026ndash;Level Health Worker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeonatal Mortality Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkilled Birth Attendance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eThe study was duly approved by the hospital's Ethics Committee (RHS/SUM/EXM/DRS/2015/07), and consent was obtained from the participants\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eJC conceived, designed and drafted the work, IV analyzed and interpreted the data, and CJ substantively revised the work\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo external funding received\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJC conceived, designed and drafted the work, IV analyzed and interpreted the data, and CJ substantively revised the work\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the management and staff of Rural Health Services of Sudan United Mission and the Nigeria Reformed Church involved in this study. We also thank the mid-level health workers trained through the Grow-Your-Own programme for their dedication and contributions, as well as the community leaders and study participants for their cooperation and invaluable insights.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed in this study are included in this published article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOkereke E, Ishaku SM, Umumeri G, Mohammed B, Ahonsi B. Reducing maternal and newborn mortality in Nigeria—a qualitative study of stakeholders’ perceptions about the performance of community health workers and the introduction of community midwifery at primary healthcare level. Hum Resour Health. 2019 Dec 23; 17(102).\u003c/li\u003e\n\u003cli\u003eNtoimo LFC, Okonofua FE, Igboin B, Ekwo C, Imongan W, Yaya S. Why rural women do not use primary health centres for pregnancy care: evidence from a qualitative study in Nigeria. BMC Pregnancy and Childbirth. 2019; 19(277).\u003c/li\u003e\n\u003cli\u003eAbimbola S, Okoli U, Olubajo O, Abdullahi MJ, Pate MA. The Midwives Service Scheme in Nigeria. PLoS Med. 2012 May; 9(5): e1001211.\u003c/li\u003e\n\u003cli\u003eMbachu C, Etiaba E, Ebenso B, Ogu U, Obinna O, Uzochukwu B, et al. Village health worker motivation for better performance in a maternal and child health programme in Nigeria: A realist evaluation. J Health Serv Res Policy. 2022 July; 27(3): 222–231.\u003c/li\u003e\n\u003cli\u003eAgunwa CC, Obi IE, Ndu AC, Omotowo IB, Idoko CA, Umeobieri ak, et al. Determinants of patterns of maternal and child health service utilization in a rural community in south eastern Nigeria. BMC Health Services Research. 2017 November; 17(715).\u003c/li\u003e\n\u003cli\u003eNational Population Commission (NPC) [Nigeria], ICF. Nigeria Demographic and Health Survey 2018. Abuja, Nigeria, and Rockville, MD:, NPC and ICF; 2019.\u003c/li\u003e\n\u003cli\u003eUnited Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels \u0026amp; trends in child mortality: report 2023.. New York: UNICEF; 2023.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Trends in maternal mortality 2000 to 2020. Geneva: WHO; 2023.\u003c/li\u003e\n\u003cli\u003eAdedokun T, Uthman OA, Bisiriyu LA. Determinants of partial and adequate maternal health services utilization in Nigeria: analysis of cross-sectional survey. BMC Pregnancy and Childbirth. 2023 June 30; 23(457).\u003c/li\u003e\n\u003cli\u003eKruk ME, Gage A, Arsenault, Jordan K, Leslie HH, Roder-DeWan. High-quality health systems in the Sustainable Development Goals era: time for a revolution. The Lancet Global Health. 2018 September; 6(11).\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global strategy on human resources for health: Workforce 2030. Geneva: WHO; 2016.\u003c/li\u003e\n\u003cli\u003eFulton BD, Scheffler RM, Sparkes SP, Yoonkyung Auh E, Vujicic M, Soucat A. Health workforce skill mix and task shifting in low income countries: a review of recent evidence. Hum Resour Health. 2011 Jan; 11(9:1).\u003c/li\u003e\n\u003cli\u003eDolea C, Stormont,, Braiche JM. Evaluated strategies to increase attraction and retention of health workers in remote and rural areas. Bull World Health Organ. 2010 May; 88(5): 379 − 85.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. World HeTask shifting: rational redistribution of tasks among health workforce teams: global recommendations and guidelines. Geneva: WHO; 2008.\u003c/li\u003e\n\u003cli\u003eScott K, Beckham SW, Gross M, Pariyo G, Rao KD, Cometto G, et al. What do we know about community-based health worker programs? A systematic review of existing reviews on community health workers. Hum Resour Health. 2018 August 16; 16(1:39).\u003c/li\u003e\n\u003cli\u003eKok C, Broerse JEW, Theobald S, Ormel, Dieleman, Taegtmeyer M. Performance of community health workers: situating their intermediary position within complex adaptive health systems. Hum Resour Health. 2017 September 2; 15(1:59).\u003c/li\u003e\n\u003cli\u003eCometto, Ford N, Pfaffman-Zambruni, Akl EA, Lehmann, McPake, et al. Health policy and system support to optimise community health worker programmes: an abridged WHO guideline. Lancet Glob Health. 2018 Dec; 6(12): e1397-e1404.\u003c/li\u003e\n\u003cli\u003eBhutta ZA, Das K, Bahl, Lawn E, Salam A, Paul K, et al. Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? Lancet. 2014 July 26; 384(9940): 347 − 70.\u003c/li\u003e\n\u003cli\u003eSingh, Sachs D. 1 million community health workers in sub-Saharan Africa by 2015. Lancet. 2013 July 27; 382(9889): :363-5.\u003c/li\u003e\n\u003cli\u003eDussault G, Franceschin. Not enough there, too many here: understanding geographical imbalances in the distribution of the health workforce. Hum Resour Health. 2016 May 27; 4(12).\u003c/li\u003e\n\u003cli\u003eRussell, Mathew, Fitts, Liddle Z, Murakami-Gold, Campbell, et al. Interventions for health workforce retention in rural and remote areas: a systematic review. Human Resources for Health. 2021 August 26; 19(103).\u003c/li\u003e\n\u003cli\u003eShikuku DN, Tanui G, Mercy W, Wanjala D, Friday J, Peru, et al. The effect of the community midwifery model on maternal and newborn health service utilization and outcomes in Busia County of Kenya: a quasi-experimental study. BMC Pregnancy Childbirth. 2020 Nov 19; 20(708).\u003c/li\u003e\n\u003cli\u003eWorld Health Organization.. Everybody’s business: strengthening health systems to improve health outcomes. Geneva: WHO; 2007.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global strategy on human resources for health: Workforce 2030. Geneva: WHO; 2016.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Increasing access to health workers in remote and rural areas through improved retention: global policy recommendations. Geneva: WHO; 2010.\u003c/li\u003e\n\u003cli\u003eDamschroder J, Aron DC, Keith E, Kirsh R, Alexander A, Lowery C. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2002 August; 4(50).\u003c/li\u003e\n\u003cli\u003eKringos S, Boerma G, Hutchinson, van der Zee, Groenewegen P. The breadth of primary care: a systematic literature review of its core dimensions. BMC Health Serv Res. 2010 March; 10(65).\u003c/li\u003e\n\u003cli\u003eFederal Ministry of Health. Task-shifting and task-sharing policy for essential health care services in Nigeria. Abuja, Nigeria:; 2014.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Increasing access to health workers in remote and rural areas through improved retention.. Geneva:; 2010.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Monitoring and evaluation of health workforce interventions. Geneva;:; 2013.\u003c/li\u003e\n\u003cli\u003eWilson NW, Couper ID, De Vries E, Reid S, Fish T, Marais BJ. A critical review of interventions to redress the inequitable distribution of healthcare professionals to rural and remote areas. Rural Remote Health. 2009 April-June; 9(2): 1060.\u003c/li\u003e\n\u003cli\u003eMcPake, Maeda A, Araújo C, Lemiere, Maghraby, Cometto G. Why do health labour market forces matter? Bull World Health Organ. 2013 Nov 1; 91(11): 841-6.\u003c/li\u003e\n\u003cli\u003eOkoroafor SC, Christmals C. Task Shifting and Task Sharing Implementation in Africa: A Scoping Review on Rationale and Scope. Healthcare (Basel). 2023 April 21; 11(8): 1200.\u003c/li\u003e\n\u003cli\u003eLawn JE, Blencowe H, Oza, You D, Lee AC, Waiswa P, et al. Every Newborn: progress, priorities, and potential beyond survival. Lancet. 2014 July 12; 384(9938): 189–205.\u003c/li\u003e\n\u003cli\u003eSerneels P, Montalvo G, Pettersson G, Lievens T, Buterae D, Kidanu A. Who wants to work in a rural health post? The role of intrinsic motivation, rural background and faith-based institutions in Ethiopia and Rwanda. Bull World Health Organ. 2010; 88: 342–349.\u003c/li\u003e\n\u003cli\u003eDiara JC. Assessment of the outcome of tasking shifting some maternal and child health services to community extension workers in a rural hospital in Ebonyi State. Ibom Medical Journal. 2025 October 1; 18(4).\u003c/li\u003e\n\u003cli\u003eDieleman M, Harnmeijer J. Improving health worker performance. Geneva: WHO; 2006.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO recommendations: optimizing health worker roles to improve access to key maternal and newborn health interventions through task shifting. Geneva: WHO; 2012.\u003c/li\u003e\n\u003cli\u003eOleribe O, Momoh, Uzochukwu S, Mbofana F, Adebiyi, Barbera T, et al. Identifying Key Challenges Facing Healthcare Systems In Africa And Potential Solutions. Int J Gen Med. 2019 Nov; 6(12): 395–403.\u003c/li\u003e\n\u003cli\u003eSpeybroeck N, Kinfu, Dal Poz, Evans B. Reassessing the Relationship Between Human Resources for Health, Intervention Coverage and Health Outcomes. Lancet. 2006; 368: 1451–6.\u003c/li\u003e\n\u003cli\u003eSherr K, Fernandes Q, Kanté M, Bawah A, Condo, Mutale, et al. Measuring health systems strength and its impact: experiences from the African Health Initiative. BMC Health Serv Res. 2017 Dec 21; 17((Suppl 3)): 827.\u003c/li\u003e\n\u003cli\u003eCampbell J, Buchan J, Cometto G, David B, Dussault, Fogstad H, et al. Human resources for health and universal health coverage: fostering equity and effective coverage. Bull World Health Organ. 2013 Nov 1; 91(11): 853 − 63.\u003c/li\u003e\n\u003cli\u003eCometto, Assegid, Abiyu G, Kifle M, Tunçalp Ö, Syed 5 S, et al. Health workforce governance for compassionate and respectful care: a framework for research, policy and practice. 2022 March; 7(3): :e008007.\u003c/li\u003e\n\u003cli\u003eUnited Nations Inter-agency Group for Child Mortality Estimation. Levels and trends in child mortality. New York: UNICEF; 2023.\u003c/li\u003e\n\u003cli\u003ePerry B, Zulliger, Rogers M. Community health workers in low-, middle-, and high-income countries: an overview of their history, recent evolution, and current effectiveness. Annu Rev Public Health. 2014; 4(35): 399–421.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Rural health workforce, Grow-your-own, Mid-level health workers, Maternal mortality, Neonatal mortality, Health systems strengthening","lastPublishedDoi":"10.21203/rs.3.rs-9389883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9389883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRural Nigeria continues to experience critical shortages of skilled health workers, contributing to persistently high maternal and neonatal mortality. \u0026ldquo;Grow-Your-Own\u0026rdquo; (GYO) workforce strategies\u0026mdash;locally recruiting and training mid-level health workers\u0026mdash;have been proposed as sustainable solutions to rural health workforce gaps. However, empirical evidence on their effectiveness in improving maternal and child health (MCH) outcomes remains limited. This study assessed the impact of a rural hospital\u0026ndash;based GYO mid-level workforce development program on key MCH indicators in Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA mixed-methods quasi-experimental study was conducted using a 10-year interrupted time-series design (5 years pre-intervention; 5 years post-intervention). Quantitative outcomes included Skilled Birth Attendance (SBA), antenatal care attendance (\u0026ge;\u0026thinsp;4 visits), Maternal Mortality Ratio (MMR), and Neonatal Mortality Rate (NMR). Segmented regression models estimated the immediate level and slope changes following program implementation. Qualitative data from in-depth interviews and focus group discussions examined the contextual mechanisms that influence outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mid-level workforce increased threefold (8 to 24 staff), with retention improving from 45% to 82%. SBA increased from 38.2% to 71.4% (+\u0026thinsp;86.9%), while ANC coverage rose from 44.7% to 78.3% (+\u0026thinsp;75.1%). MMR declined from 612 to 382 per 100,000 live births (\u0026ndash;37.6%), and NMR decreased from 41 to 23 per 1,000 live births (\u0026ndash;43.9%). Interrupted time-series analysis demonstrated significant post-intervention level and slope changes across all primary outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Improvements exceeded concurrent national trends by two- to fourfold. Qualitative findings highlighted enhanced community trust, improved cultural alignment, reduced staff turnover, and increased service accessibility as key facilitators.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe GYO workforce model was associated with substantial improvements in maternal and neonatal outcomes in a rural Nigerian setting. Locally anchored workforce development strategies may offer a scalable, equity-oriented approach to strengthening primary health systems in resource-constrained settings.\u003c/p\u003e","manuscriptTitle":"Assessing the effectiveness of a “Grow-Your-Own” mid-level health workforce development program in improving maternal and child health indices in rural Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:39:20","doi":"10.21203/rs.3.rs-9389883/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T12:57:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T10:27:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T10:27:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-11T17:46:04+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":"2ccfdcc1-e8fc-46a5-b524-42081cd9919d","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T19:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:39:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9389883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9389883","identity":"rs-9389883","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}