Frontiers in Public Health

Based on the 2023 Global Burden of Disease (GBD) database, this study analyzed the global burden of preterm birth from 1990 to 2023 and predicted its development trend by 2050, while exploring the disparities in disease burden across regions with different Socio-demographic Index (SDI) levels, income groups and countries. A retrospective trend analysis was conducted to collect data on preterm birth incidence, prevalence, death and disability-adjusted life years (DALYs) in 204 countries and regions worldwide from 1990 to 2023 from the GBD 2023 database. ARIMA model (p=2,d=1,q=1) and grey prediction model (GM(1,1)) were combined to predict the preterm birth burden from 2023 to 2050. In 2023, preterm birth was the primary cause of the global neonatal disease burden, with its four core indicators significantly higher than other neonatal diseases. From 1990 to 2023, the global incidence, death and DALYs of preterm birth decreased to 0.91, 0.44 and 0.52 times of the 1990 levels respectively, while the prevalence increased to 1.54 times of the baseline. Projection results showed that by 2050, the incidence, death and DALYs of preterm birth would drop to 0.79, 0.08 and 0.32 times of the 2023 levels, and the prevalence would rise to 1.23 times of 2023. Low SDI regions, lower-middle income countries, as well as India and Nigeria, bore the heaviest disease burden. Over the past three decades, the global acute health burden of preterm birth such as death has decreased notably, but the continuous rise in prevalence and severe regional and age disparities remain prominent public health challenges. The 0-6 days and 6-11 months age groups are the key time windows for preterm birth intervention. It is urgent to implement targeted prevention and control measures for low SDI regions and lower-middle income countries to reduce the global burden of preterm birth.

Background High body mass index (BMI) presents a serious and ongoing global health challenge. However, the difficulty of high BMI intervention has not yet been systematically evaluated. Methods We developed a Generative Artificial Intelligence Meta-Evaluation (GAME) framework, which integrated 18 indicators from 4 dimensions, including "Macro-System Level", "Socio-Cultural Level", "Community-Family Level", and "Individual Level" to evaluate the difficulty of high BMI intervention across 226 locations. The GAME framework applies 8 leading AI models to generate intervention difficulty scores (IDS) of each indicator on a scale from 1 to 5, with higher scores indicating greater difficulty. Meta-analysis was conducted to derive combined scores, evaluate the heterogeneity and sensitivity. Final intervention difficulty scores were calculated as the weighted sum of all 18 indicators. Additionally, SHapley Additive exPlanation (SHAP) values were used to evaluate the importance of each indicator in determining the intervention difficulty. Results The global difficulty of high BMI intervention shows significant imbalance. Norway (IDS = 1.48) exhibited the easiest intervention, while Yemen (IDS = 4.56) faced the greatest challenge. Regions such as Western Europe, Australasia, and High-income Asia Pacific showed lower intervention difficulty, reflecting there are mature public health frameworks, supportive social-cultural environments for healthy lifestyles, and high levels of health awareness. On the contrary, countries in North Africa and Middle East, South Asia, Oceania, and Sub-Saharan Africa faced higher intervention challenges, suggesting the need for long-term, collaborative efforts from multiple sectors. Among the 18 indicators, "Cognition and Awareness" has the most significant impact on intervention difficulty, with the SHAP value of 31.03, followed by "Family life and cognitive patterns" (18.08) and "Health Care System" (11.7). Furthermore, the IDS for high BMI was significantly correlated with Socio-Demographic Index (SDI). Higher SDI values were associated with easier interventions. Finally, the independent external empirical verification demonstrated high consistency between intervention difficulty and increase in annual prevalence of obesity, population mean BMI, and national policies. It supported the GAME framework to characterize global heterogeneity in high BMI intervention challenge. Global results were freely available at http://www.deepburden.com/high-bmi. Conclusion The difficulty of high BMI intervention varies widely across countries and regions, highlighting the need for comprehensive strategies and governance to address the growing health issue effectively.

Ambulatory Care Sensitive Conditions (ACSCs) are conditions for which effective and timely primary health care (PHC) can prevent hospitalizations. They are widely used as a proxy indicator of access to and quality of PHC. Despite their relevance, evidence from Central America remains scarce. This study aimed to quantify the burden, describe the epidemiological profile, and assess temporal trends of ACSCs hospitalizations in Honduras from 2014 to 2024. We conducted a retrospective observational study using national administrative hospital discharge data from all Ministry of Health hospitals. ACSCs were defined using a standardized list of 20 diagnostic groups based on ICD-10 codes. We estimated percentages and sex-age-standardized hospitalization rates per 10,000 inhabitants. Clinical indicators included length of stay (LOS) and in-hospital fatality rates. Temporal trends were evaluated using joinpoint regression models to estimate annual percent changes (APC). Analyses included stratification by age, sex, and disease category. A total of 4,023,944 hospitalizations were analyzed, of which 547,486 (13.6%) were classified as ACSCs. The overall sex-age-standardized rate was 54.1 per 10,000 inhabitants. ACSCs standardized rates increased between 2014 and 2018 (APC: 2.7%; 95% CI: -2.4; 15.2), declined sharply between 2018 and 2021 (APC: -17.8%; 95% CI: -30.6; -10.3), and increased again between 2021 and 2024 (APC: 15.9%; 95% CI: 4.6; 37.6). Despite this rebound, rates remained below pre-pandemic levels. ACSCs were concentrated among children under 5 years (27.7%) and adults aged 60 years and older (29.9%). Noncommunicable diseases accounted for 56.8% of cases, with diabetes mellitus as the leading cause. Compared with non-ACSCs hospitalizations, ACSCs were associated with longer LOS (4.9 vs. 3.9 days; p <0.001) and higher in-hospital fatality rates (2.4% vs. 1.7%; p <0.001). ACSCs hospitalizations constitute a substantial burden in Honduras and reflect persistent gaps in PHC performance. Strengthening PHC resilience and capacity, particularly for chronic disease management and vulnerable populations, is essential to reduce avoidable hospitalizations and improve health system efficiency and equity.

Background: Skin-related neglected tropical diseases (skin NTDs) continue to affect people living in remote communities of endemic countries, particularly in regions with limited access to dermatological care. This operational research evaluated the impact of the eSkinHealth app, a digital health tool designed to enhance case management of skin NTDs and other skin diseases in Cote d'Ivoire. The eSkinHealth app functions as a portable electronic medical record and a platform for teledermatology, connecting frontline healthcare workers to remote specialists. Methodology/Principal Findings: The study was conducted across sixteen primary health centers (PHCs) in the Sinfra and Bouafle districts, regions endemic for skin NTDs. Using a before-and-after implementation design, baseline data were collected from paper registries and compared with data captured through the app. The primary objective was to assess changes in skin disease detection and diagnosis, while also evaluating usability, acceptability, and feasibility of the tool among healthcare workers. A total of 1,766 patients were included in the analysis (mean age 22.8 years; 55% male). During the intervention period, skin NTD registrations increased significantly from 30 to 91 cases (p < 0.01). Buruli ulcer cases rose from 6 to 14 (p = 0.05), scabies from 24 to 70 (p = 0.13), and other NTDs such as leprosy, lymphatic filariasis, and yaws were newly detected and documented. In contrast, registrations of non-NTD skin diseases decreased from 662 to 472 cases (p < 0.01); however, the proportion of non-NTD cases which received diagnostic confirmation increased markedly, from 0% at baseline to 94% during the intervention period (p < 0.01). Qualitative interviews with nurses and community health workers highlighted improvements in diagnostic accuracy, patient engagement, and confidence in daily practice, while also noting persistent challenges such as stigma, transportation barriers, technical difficulties, and patient concerns about privacy. Conclusions/Significance: The integration of the eSkinHealth app into routine PHC services proved effective in enhancing diagnostic capacity for skin NTDs in resource-limited settings. However, capturing other skin diseases proved more difficult given their high prevalence. While the app demonstrated clear benefits in improving diagnostic rates and healthcare worker confidence, persistent challenges such as technical issues and patient concerns about privacy need to be addressed for future scalability. As with many digital tools, further refinement will be an ongoing process, and the lessons learnt from this study may provide valuable guidance for similar initiatives in comparable settings.

Background: Nursing education in conflict-affected settings faces significant disruptions that compromise the preparation of a competent and resilient workforce. In regions such as Iraq, prolonged instability, resource constraints, and fragmented health systems challenge traditional educational models, necessitating innovative and context-responsive approaches to ensure continuity, quality, and equity in nursing training. Purpose: This study aimed to explore innovative strategies in nursing education within conflict-affected settings and to examine their implications for leadership development, health policy reform, and the advancement of health equity. Methods: A cross-sectional descriptive study was conducted among undergraduate nursing students across selected universities in the Nineveh Governorate, Iraq, during the 2025-2026 academic year. Data were collected using a structured, self-administered questionnaire designed to assess students educational experiences, engagement with digital learning approaches, perceived barriers, and attitudes toward innovation in nursing education. The instrument captured multiple dimensions of the learning environment, including access to educational resources, institutional support, and exposure to blended and technology-enhanced learning. Descriptive and inferential statistical analyses were performed using SPSS (version 28), including frequency distributions, chi-square tests, and binary logistic regression modeling to identify key predictors of positive educational outcomes, such as engagement, satisfaction, and perceived clinical readiness. Results: The findings indicate that, although students demonstrated a high level of motivation to engage with innovative learning approaches, notable gaps remained in access to digital resources, faculty preparedness, and institutional support. A majority of participants reported engagement with blended and technology-enhanced learning, which was significantly associated with higher levels of engagement, improved critical thinking, and greater perceived clinical readiness (p < 0.001). Multivariable analysis identified institutional support, digital learning access, and learner-centered teaching strategies as significant predictors of positive educational outcomes. Students with access to digital learning resources and supportive educational environments were more likely to report higher levels of satisfaction and competence. Conclusions: Innovating nursing education in conflict-affected settings is essential to building a resilient and future-ready nursing workforce. Integrating digital technologies, flexible learning models, and competency-based approaches can enhance educational outcomes despite contextual constraints. Implications for Nursing Practice and Policy: Strategic investment in nursing education infrastructure, faculty development, and digital transformation is critical to strengthening health systems in fragile contexts. Policymakers and academic leaders must prioritize inclusive, scalable, and sustainable educational reforms to promote health equity and empower nurses as key agents of system-level change.

Comprehensive and responsive interventions are increasingly prioritized to address the diverse and evolving health challenges faced by mothers and children during the first 1,000 days of life. However, evidence remains limited on how such interventions can be operationalized in low-resource settings without overstretching frontline health workers. We developed a comprehensive yet flexible community-based intervention, the Healthy Future program, which integrates a stage-based maternal and child health curriculum with mHealth-enabled infrastructure to deliver targeted, stage-based support through home visits in low-resource settings. We evaluated its impact through a cluster-randomized controlled trial across 119 rural townships in China. The program demonstrated improvements across multiple health, behavioral, and intermediate outcomes, including young child feeding practices, caregiving knowledge, maternal mental health, and perceived social support. Overall, this study illustrates a move beyond stand-alone interventions toward a scalable, multidimensional delivery model capable of providing comprehensive, flexible, and timely support to mothers and children in low-resource communities while remaining feasible for large-scale implementation.

Introduction Gender norms and roles are important determinants of physical and mental health in the key period of adolescence. Yet, the gendered pathways to mental health in adolescents are not fully understood. Using a conceptual framework for global adolescent mental health that we developed based on a Delphi process, we empirically investigated the associations between six gender-related constructs and adolescent mental health. Methods We used cross-sectional Gender and Adolescence: Global Evidence (GAGE) data from Ethiopia (2020) to explore the associations between sex, gender norms, psychological competencies, gender attitudes, gender roles, with the latter two also serving as mediators, and psychological distress (GHQ-12), using Structural Equation Modelling (SEM). Results The SEM model contained measurements from 1,584 adolescents, including 843 girls and 741 boys, with a median age of 13 years. Out of 14 pathways tested, we found statistically significant associations between psychological competencies and psychological distress; sex and gender attitudes; and between gender norms and psychological competencies, gender attitudes, and gender roles. Hence, the gender-related constructs were mostly associated with each other, rather than with psychological distress. Conclusion The gender-related constructs are strongly interrelated, thereby attenuating their individual effects on psychological distress. The interplay of gender-related constructs should be considered when developing interventions to promote mental health in adolescents.

Background: In crises, hospitals must rapidly shift from routine operations to structured crisis management, requiring the activation of an incident command system. However, empirical insight into their operational functioning during activation remains limited. Goal: to identify operational enablers and barriers influencing effective crisis response. Methods: Prospective cross-sectional, qualitative, single-center study conducted after a table-top exercise within a hospital incident command system at a tertiary care university hospital (NCT06913010). Data was collected through semi-structured interviews, participant observation, and document analysis, and analyzed using a narrative-phenomenological approach. Results: Nineteen participants were included. Analysis identified nine thematic clusters shaping operational performance: (1) structure and roles; (2) communication; (3) decision-making and prioritization; (4) information management; (5) infrastructure and technology; (6) personnel and organization; (7) training, exercises, and team dynamics; (8) documentation; and (9) external communication and media. Enablers included clear role definition, structured communication, phased decision-making, and regular training. Barriers included role ambiguity, fragmented communication, insufficient prioritization, infrastructure limitations, and staffing constraints. Conclusion: Preparedness frameworks are necessary but insufficient as stand-alone approaches, as operational execution determines real-world performance. Recurring deficits included unclear roles, inconsistent communication, weak prioritization, and gaps in infrastructure and personnel. A limited set of standardized practices - including a clear separation od roles, leadership intent, closed-loop communication, explicit decision cycles from information gathering to structuring to decision-making, checklists, visualization, central information management, and rapid "80% decisions"-substantially enhanced performance. Mission command (Auftragstaktik) further enabled adaptive, coordinated action. Strengthening hospital incident command is a key lever for achieving system-level resilience in crises.

BackgroundChina has implemented policies to strengthen its pharmacist workforce since the 2009 healthcare reform, yet a comprehensive evaluation of their long-term systemic effects is lacking. ObjectiveTo systematically analyze the evolution of Chinas pharmacist workforce in healthcare institutions from 2007 to 2023 across four dimensions: quantity, quality, structure, and distribution, providing an empirical foundation for policy optimization. MethodsA retrospective analysis was conducted using longitudinal data from the China Health Statistics Yearbooks. Trends were delineated via descriptive statistics. Equity and spatial evolution were assessed using the Gini coefficient, Theil index decomposition, and spatial autocorrelation analyses (Global Morans I and hotspot analysis). ResultsFrom 2007 to 2023, the total number of pharmacists increased from 357,700 to 569,500 (average annual growth: 2.2%). This growth lagged behind physicians (4.6%) and nurses (7.4%),causing the pharmacist-to-physician ratio to decline from 1:5.15 to 1:8.39. The workforce showed trends of feminization (female proportion rose from 59.7% to 70.8%) and aging. While quality improved, 51.1% still held an associate degree or below, and only 6.6% held senior titles. Equity analysis revealed the provincial Gini coefficient improved from 0.145 to 0.093. Theil index decomposition confirmed intra-provincial disparities as the primary inequality driver. Spatial analysis showed a non-significant global Morans I by 2023 (0.154, P*>0.05), down from 0.254 (P<0.01) in 2007. Hotspot analysis confirmed this transition, revealing a contraction of high-confidence clusters and a trend toward balanced distribution. ConclusionsChina has made measurable progress in expanding pharmacist workforce size and improving inter-provincial equity since 2007. However, persistent structural challenges remain: relative workforce contraction compared to other health professions, an aging demographic, a shortage of senior talent, and significant intra-provincial inequity. Future policies must prioritize optimizing workforce structure and enhancing clinical service capabilities to catalyze a shift toward patient-centered pharmaceutical care. HighlightsO_LIFirst longitudinal study (2002-2023) tracking Chinas institutional pharmacist workforce post-healthcare reform, revealing a critical structural shortage. C_LIO_LIPharmacist growth rate (2.2% annually) severely lagged physicians (4.6%) and nurses (7.4%), causing the pharmacist-to-physician ratio to plummet from 1:5.15 to 1:8.39. C_LIO_LI69.2% of Chinas drug market (prescription drugs) is managed by only 569,500 institutional pharmacists--175,000 fewer than retail pharmacists, exposing a critical workload imbalance. C_LIO_LISpatial disparity paradox: Gini coefficient improved to 0.093 (high equity), yet Theil decomposition revealed intra-provincial (urban/rural) gaps as the primary driver of inequality. C_LIO_LIHigh-level talent deficit: Despite quality gains, only 6.6% hold senior titles and 6.1% have masters degrees--a bottleneck for advancing clinical pharmaceutical care. C_LI

Background: Despite the growing global burden of multimorbidity, the patterns of disease combinations, have not been extensively categorized. We aimed to explore the predictors, health consequences, and patterns of discordant and concordant multimorbidity. Methods: We used the 2018 China Health and Retirement Longitudinal Study (CHARLS), a representative database of adults aged >45 years from China. We conducted logistic regression analyses to assess the likelihood of having discordant (conditions from different disease systems) versus concordant (only cardiometabolic, or only respiratory diseases) multimorbidity, and to compare the health status and healthcare utilization between patients with discordant and concordant multimorbidity. Latent class analysis (LCA) was applied to both the entire sample and to patients with discordant multimorbidity to identify clusters of disease combinations. Results: The sample included 1668 patients with concordant (mainly cardiometabolic), and 7306 patients with discordant, multimorbidity. Female patients, patients living in rural settings, former and current smokers, and patients engaging in high-intensity physical activity, were more likely to have discordant instead of concordant multimorbidity. Depression, limitations in daily activities, poor self-reported health, and frequent healthcare use were more common in patients with discordant than concordant multimorbidity. The LCA identified five clusters when all multimorbid patients were included (cardiometabolic, arthritis-digestive, respiratory, multisystem, and arthritis-hypertension classes), and four clusters when restricted to discordant multimorbidity (digestive, arthritis-cardiometabolic, respiratory, and multisystem classes). Conclusion: Discordant multimorbidity is associated with poorer health and increased use of healthcare. Cardiometabolic diseases, arthritis, and digestive diseases have a central role in defining disease patterns.

Introduction -- Emergency Medical Dispatch Systems (EMDS) can reduce delays in accessing emergency care by providing structured communication, triage, and coordination. However, such systems remain absent or underdeveloped in most low- or middle-income countries (LMICs). This study aimed to establish international consensus on essential EMDS components to inform global guidance. Methods -- We convened a multidisciplinary expert group to draft a preliminary list of essential components for three EMDS levels reflecting resource availability and system maturity. We then conducted a three-round Delphi with international experts to reach consensus on core EMDS components. Components which had [&ge;]75% agreement were included, those with [&ge;]75% disagreement were excluded. Components not achieving consensus by Round 3 were removed. Results were analysed overall and stratified by respondents' country income level. A subsequent online expert meeting resolved inconsistencies and finalised the component list. Results -- The expert group generated 111 components for each of three EMDS levels (Foundational, Emerging, and Established) spanning 11 operational domains. Of the 68 experts invited to the Delphi, 43 participated in Round 1 and 30 in Round 3. Across all Delphi rounds, 289 components reached consensus for inclusion. The consensus resulted in a final list of 227 components (63 Foundational, 84 Emerging, and 80 Established). Consensus agreement clustered around core EMDS domains including communication, structured call-taking and prioritisation, advice-giving, resource dispatch and tracking, and foundational governance and data functions, whereas items showing either non-consensus or consensus disagreement were typically technology-dependent or context-specific. Conclusions -- This international consensus offers guidance for EMDS development across diverse resource settings and provides a scalable roadmap to strengthen emergency care systems.

Care workers are central to health systems and the broader care economy, but they often lack the legal protections afforded to other workers. Furthermore, there currently exists no single legal definition of "care worker" under any binding or non-binding international legal instrument. Drawing on the WHO Global Health and Care Worker Compact, we analyzed whether national laws and policies in 10 countries protect care workers. Using comparative legal methods and primary source legal and policy documents, we evaluated both care worker coverage and alignment with four indicators: guaranteed access to protective equipment, protection against discrimination on internationally recognized grounds, unemployment insurance, and the right to join independent unions. We reviewed 43 laws and policies and found that 56% fully or partially met the relevant indicator criteria. The United Kingdom was the only country meeting all four indicators. Overall, we found while many countries recognize these protections in law, care workers are often left outside their coverage, underscoring the need for clearer legal recognition and more inclusive worker protections.

Since the U.S. 2013/14 influenza season, the CDC's FluSight Challenge has provided a platform for evaluating influenza forecasting models and fostering collaboration across institutions. The Challenge aims to improve the science and enhance the utility of infectious disease forecasts for public health decision making. We analyzed ten years of submitted forecasts (2014/15-2019/20 (influenza-like illness seasons) and 2021/22-2024/25 (hospital admissions seasons)) across a range of model types, including statistical, mechanistic, machine learning, and hybrid models. Influenza-like illness (ILI) forecasts were evaluated using the exponentiated logarithmic score (skill metric) while hospital admissions forecasts were evaluated using the log transformed relative Weighted Interval Score. Corresponding potential performance differences were assessed using Wilcoxon rank-sum tests, and associations with team participation history were evaluated using Spearman's rank correlation. Model performance varied by season, and no single model type consistently outperformed others. In ILI seasons, statistical models generally performed better than mechanistic and machine learning models, though consistent differences were not observed in more recent hospital admissions seasons. Ensemble forecasts showed better overall performance across seasons, and the CDC's FluSight ensemble ranked among the top-performing forecasts every year. We also found a positive correlation between forecast accuracy and the number of years a team participated in the Challenge, with statistically significant associations in four seasons. These findings highlight the benefits of ensemble approaches and sustained engagement in improving forecasting performance, while also underscoring the continued value of forecast evaluation before and following the COVID-19 pandemic. Insights from the FluSight Challenge can guide future infectious disease forecasting efforts and support more effective public health preparedness.

Aims Moral distress has been studied across many health arenas; however, public health has often been overlooked. Canada is facing a healthcare crisis with a significant number of staff leaving the healthcare field. This study explores the experiences of moral distress in public healthcare practitioners across Canada. Better understanding these experiences can provide insights into how to support staff and prevent attrition in public health. Methods This was a cross-sectional qualitative study. Fifteen in-depth interviews were conducted between May and July 2023, through remote and in person methods. Participants were from nursing, social work, medicine, and dietetics, all working in public health across Canada. Iterative thematic analysis was used. Emergent themes were compared within and across data sets and by participant age and years of experience. Results/Findings Experiences that contributed to moral distress included systemic powerlessness, political and ideological overreach, unethical work environments and undervalued expertise. Years of experience and diversity in gender and ethnicity impacted how practitioners navigated moral distress. Experiences where practitioners felt actions went against their values increased during the pandemic, contributing to moral injury. Conclusions This study situates the unique position of public health within the health system and explores experiences of moral distress both during and outside the COVID-19 pandemic. While the pandemic brought the concept of moral distress to the forefront of many peoples minds, these experiences existed prior. Addressing the underlying causes will contribute to establishing approaches to support public health practitioners suffering from moral distress and injury.

Background: Higher education has been transformed by the rapid integration of generative artificial intelligence (GenAI) tools into academia. The objective of the present study was to examine how and for what purposes senior undergraduate dental students use GenAI tools in academic assignments. Methods: This cross-sectional study uses data from three written assignments submitted by two consecutive cohorts of graduating fourth-year dental students at the Faculty of Dentistry at the University of British Columbia, for a total of 120 students. The assignments focused on different subjects where students had to offer their views, including community water fluoridation. When using GenAI, students were asked to disclose whether and how such tools were used, and for what purpose. Descriptive statistics (e.g., means, frequencies, and proportions) were conducted via IBM SPSS Statistics (Version 27.0). Results: From the two cohort of students, 102 (85%) disclosed the use of GenAI tools in at least one assignment; of these, 69 (67.6%) reported using these tools in all three assignments. ChatGPT was by far the most frequently used GenAI tool, reported by 89 students (87.2%). Nine students (8.8%) did not specify which tool they had used. The majority of the students (91.2%, n = 93) reported using GenAI for proofreading or grammatical editing. About 9.8% of the students (n = 10) reported more substantive uses, such as relying on GenAI to generate in part or in full the assignment, and/or assessing the credibility of references. Conclusions: In our study, the use of GenAI tools was highly prevalent among senior undergraduate dental students for editorial purposes. A smaller but notable proportion of students engaged in more substantive uses that may carry academic and ethical risks. There is a need for structured AI literacy training and clear, dentistry-specific guidelines to promote responsible and transparent use while safeguarding critical thinking, academic integrity, and professional judgment in dental education.

Clinical decision-making is a critical competency for nurses, particularly in resource-constrained healthcare systems where frontline practitioners must integrate clinical knowledge, judgment, and contextual constraints to ensure optimal patient outcomes. Although prior research highlights the benefits of artificial intelligence (AI)-supported learning and individual competencies, it largely assumes a direct relationship between technological support and decision quality, overlooking the cognitive-regulatory mechanisms through which such effects occur. This study addresses this gap by examining self-care competence as a mediating pathway linking perceived AI-based learning support to enhance clinical decision-making among nurses in Benue State, Nigeria. A descriptive cross-sectional design was employed, with data collected from 600 registered nurses across public and private healthcare facilities using the Self-Care Competence Scale (SCCS), AI-Based Learning Support Scale (PAILS) and the Clinical Decision-Making Scale (CDMNS). Data were analyzed using structural equation modelling (SEM) to test direct and indirect relationships, complemented by bootstrapped mediation analysis and rigorous assessment of common method bias through Harmans single-factor test and full collinearity variance inflation factors, ensuring robustness of the findings. Results indicated moderately high levels of self-care competence, perceived AI-based learning support, and enhance clinical decision-making skills. Self-care competence and AI-based learning support significantly predicted clinical decision-making, with self-care competence partially mediating this relationship and the model explaining 58 percent of the variance. The findings extend theory by demonstrating that AI-supported learning enhances enhance clinical decision-making not directly, but through nurses cognitive and psychological readiness, positioning self-care competence as a central mechanism in evidence-based practice.

Essential medicines are one of the cornerstones of financial protection and health equity. The REMEDIAR Program is an initiative of the Argentine Ministry of Health aimed at ensuring free access to essential medicines for the uninsured at the point of care in primary healthcare centers (PHC). This study analyzes the financing, procurement, and distribution of this program over two decades (2002 to 2024). It evaluates how the program's capacity to navigate economic and political challenges ensured an uninterrupted supply of essential drugs at the primary healthcare level in a federal country where health services are devolved to provinces. We adopted a mixed-methods approach to examine the duality between international concessional loans and domestic treasury funding. Findings reveal that while international financing enhanced predictability and efficiency, reducing procurement timelines from 458 to 235 days, it also constrained domestic planning through external conditionalities. Conversely, while national centralized procurement achieved superior price efficiency and lower dispersion, it faced rigidities in adapting to local needs. Territorial distribution analysis confirms that REMEDIAR reduced access barriers for vulnerable households without formal insurance. However, the program entered a stabilization phase, failing to consolidate robust coordination with subnational policies, becoming entrenched in its own operational logic. The study concludes that program effectiveness depends not only on resource volume but on management quality. To guarantee long-term sustainability, transition to national financing requires profound institutional redesign. This must integrate operational capacities with federal coordination and domestic regulations, ensuring that the primary healthcare supply chain remains resilient to macroeconomic volatility and political shifts, aligned with sub-national strategies.

The rapid aging of the population in urban China has led to a significant increase in empty-nest households, necessitating a rigorous evaluation of residential environment suitability. Grounded in Person-Environment Fit theory, this study develops and validates a multidimensional Aging-Suitability Index (ASI) specifically for urban empty-nest seniors. We analyzed survey data from 753 participants across 19 provinces using Partial Least Squares Structural Equation Modeling (PLS-SEM). The comprehensive structural model demonstrated robust explanatory power (R{superscript 2} = 0.754). The results reveal a hierarchical mechanism of needs: safety features and physical design serve as the survival foundation, exerting the most substantial direct effects on overall suitability. Accessibility was found to enhance suitability primarily by fostering perceived independence, indicating a psychological mechanism of empowerment (Variance Accounted For = 67.35%). Furthermore, intelligent technology and social support function as complementary resources that improve the environment-person fit. These findings suggest that aging-in-place interventions should prioritize mandatory safety upgrades while integrating accessibility modifications to sustain functional autonomy for independent seniors.

Safety net healthcare delivery organizations (SNOs) serve vulnerable populations but face persistent challenges in adopting new technologies, including AI. While systematic barriers to technology adoption in SNOs are well documented, little is known about how AI is implemented in these settings. This study explored real-world AI adoption in SNOs, focusing on identifying barriers encountered across the AI lifecycle and strategies used to overcome them. Five SNOs in the U.S. participated in a 12-month technical assistance program, the Practice Network, to implement AI tools of their choosing. Observed barriers and mitigation strategies were documented throughout program activities and, at the conclusion of the program, reviewed and refined with participants using a participatory research approach to ensure findings reflected lived experiences and organizational contexts. Key barriers emerged during the Integration and Lifecycle Management phases and included gaps in AI performance evaluation and impact assessments, communication with patients about AI use, foundational AI education, financial resources for purchasing and maintaining AI tools, and AI governance structures. Effective strategies for addressing these barriers were primarily supported through centralized expertise, structured guidance, and peer learning. These findings provide granular, actionable insights for SNO leaders, offering guidance for anticipating barriers and proactively planning mitigation strategies. By including SNO perspectives, the study also contributes to the broader health AI ecosystem and underscores the importance of participatory, collaborative approaches to support safe, effective, and ethical AI adoption in resource-constrained settings. Author SummarySafety net organizations (SNOs) are healthcare systems that primarily serve low-income and underinsured patients. While interest in artificial intelligence (AI) in healthcare has grown rapidly, little is known about how these organizations experience AI adoption in practice. In this study, we partnered with five SNOs over a 12-month program to document the challenges they encountered when implementing AI tools and the strategies they used to address them. We worked closely with SNO staff throughout the process to ensure our findings reflected their lived experiences with AI implementation. We found that the most common challenges arose when organizations tried to integrate AI into daily operations and monitor and maintain those tools over time. Specific barriers included difficulty evaluating whether AI was performing as expected, limited guidance on communicating with patients about AI use, a lack of resources for staff training, limited financial resources, and the absence of formal governance structures. Successful strategies for overcoming these challenges drew on shared knowledge and structured support provided by the program, as well as learning from peer organizations. These findings offer practical guidance for SNO leaders planning or managing AI adoption, and contribute to a broader conversation about what is required to implement AI safely and effectively in healthcare settings that serve the most medically and socially vulnerable patients.

In the context of global health, the ability of frontline primary health providers to identify potential Drug-Drug Interactions (DDIs) is a critical component of patient safety. This is particularly true in settings like Tanzania, where drug dispensers often serve as the primary point of contact for patients. In this study, we establish a baseline for drug decision-making capabilities across multiple cadres of healthcare providers in Kibaha, Tanzania. We specifically distinguish between the ability to recognize safe drug combinations versus harmful ones. The findings reveal a critical asymmetry in provider performance: while professional training improves the recognition of safe combinations, it provides no advantage over lay intuition (and in some cases, a significant disadvantage) in detecting potentially harmful interactions.