Risk Management and Healthcare Policy最新文献

{"title":"Multisystem Influences on Mental Health Risk Among Urban Older Adults: An Ecological Systems Theory Perspective.","authors":"Juan Luo, Li Zheng, Chiyue Huang","doi":"10.2147/RMHP.S595339","DOIUrl":"10.2147/RMHP.S595339","url":null,"abstract":"<p><strong>Introduction: </strong>Mental health risk among urban older adults has become an important public health and social policy issue in the context of rapid population aging in China. Existing studies have often focused on single factors and have lacked an integrated analysis of multi-level determinants within a unified theoretical framework. This study examined the associations of ecological system factors with mental health risk among urban older adults.</p><p><strong>Methods: </strong>Data were drawn from the 2020 China Longitudinal Aging Social Survey (CLASS). Guided by Bronfenbrenner's ecological systems theory, this study constructed an analytical framework covering the micro-, meso-, exo-, macro-, and chrono-system levels. Ordered logistic regression, stepwise cumulative regression, bootstrap mediation analysis, and robustness tests were used to examine the associations of social support, activity participation, community environment, institutional environment, and risk events with mental health risk, as well as subgroup heterogeneity.</p><p><strong>Results: </strong>Higher levels of social support and more frequent activity participation were associated with higher mental health risk. Better community and institutional environments were associated with lower mental health risk, whereas risk events were significantly associated with higher risk. Mediation analysis suggested that activity participation was indirectly associated with mental health risk through risk events, while the association between community environment and mental health risk was attenuated after risk events were taken into account. Heterogeneity analysis showed substantial differences across gender, age, income, and educational groups.</p><p><strong>Discussion: </strong>Mental health risk among urban older adults was closely related to multi-level ecological factors. Policy efforts should focus on improving the quality of support, strengthening community services and institutional protection, and adopting stratified interventions for different groups. Given the cross-sectional design, the findings should be interpreted as associations rather than causal relationships.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"595339"},"PeriodicalIF":2.6,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13265259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Carbon Consumption Intention as Risk Management: Evidence from China on Theory of Planned Behavior, Social Influence, and the Moderating Role of Environmental Concern.","authors":"Ke Liu","doi":"10.2147/RMHP.S603901","DOIUrl":"10.2147/RMHP.S603901","url":null,"abstract":"<p><strong>Purpose: </strong>Climate change and environmental degradation have become important sources of public and environmental risk. From a risk-management perspective, low-carbon consumption intention may serve as an upstream behavioral pathway for mitigating carbon-related risks. This study integrates the theory of planned behavior and social influence theory to examine the determinants of low-carbon consumption intention in China and the moderating role of environmental concern.</p><p><strong>Methodology: </strong>The study conducted a survey that consisted of 333 participants recruited via an online questionnaire platform in China and examined the relationships among attitude, subjective norms, perceived behavioral control, peer imitation, endorsement influence, and low-carbon consumption intention. Confirmatory factor analysis was performed using AMOS 29 to assess the measurement model, and hierarchical ordinary least squares regression was conducted using SPSS 27 to test the proposed hypotheses and interaction effects.</p><p><strong>Results: </strong>Regression results indicated that attitude and peer imitation were significantly and positively associated with low-carbon consumption intention, whereas subjective norms, perceived behavioral control, and endorsement influence were not significant predictors. Environmental concern was positively associated with low-carbon consumption intention and significantly weakened the effects of all five antecedents on it.</p><p><strong>Conclusion: </strong>The findings suggest that low-carbon consumption intention is shaped by favorable individual evaluations and visible peer practices. Interventions promoting low-carbon lifestyles should emphasize attitude formation and peer-based social cues, while tailoring communication strategies according to individuals' levels of environmental concern.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"603901"},"PeriodicalIF":2.6,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13262593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Medical Record Quality in Psychiatric Care Through PDCA Cycle Management.","authors":"Li Pan","doi":"10.2147/RMHP.S603196","DOIUrl":"10.2147/RMHP.S603196","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the effect of Plan-Do-Check-Act (PDCA) cycle management on psychiatric medical record quality.</p><p><strong>Methods: </strong>This pre-post intervention study implemented PDCA from February 2023. A time-series analysis compared monthly Class A record rates (≥90% on a standardized checklist) across 12 months post-implementation (Feb 2023-Jan 2024). Using stratified random sampling by department, 150 records per group were selected: pre-intervention control (before 2023), observation group 1 (Feb 2023-Jan 2024, comprehensive review), and observation group 2 (Jan-Dec 2025, spot-check review). Fourteen ward physicians rated satisfaction pre- and post-intervention. Possible confounders (department, time) were addressed via stratified sampling and time-series segmentation. Statistical analyses included ANOVA with effect sizes (η²) for group comparisons and paired <i>t</i>-test with Cohen's d for satisfaction scores.</p><p><strong>Results: </strong>After implementation of the PDCA cycle, the monthly Class A record rate remained above 90% (range: 94.49-98.48%). Statistically significant between‑month differences were observed at several time points (eg, April and September 2023, both p<0.01). One‑way ANOVA indicated a statistically significant difference among the three groups (F=57.59, <i>p</i><0.01, η²=0.205). Quality scores were higher in both observation groups compared with the control group (observation group 1: 95.01±2.44 vs control: 92.26±3.58; observation group 2: 95.29±1.79 vs control: 92.26±3.58; both <i>p</i><0.01), whereas no significant difference was found between the two observation groups (<i>p</i>>0.05). Physician satisfaction scores also showed a statistically significant increase (97.29±2.09 vs 85.14±4.35; t = -12.02, <i>p</i><0.01, Cohen's d = -3.213). In the present study, the Class A rate was maintained above 90% and physician satisfaction was higher after PDCA implementation.</p><p><strong>Conclusion: </strong>In this single‑center pre‑post study, implementation of the PDCA cycle was associated with improvements in medical record quality and physician satisfaction. Further research is needed to confirm these findings in other settings.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"603196"},"PeriodicalIF":2.6,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Robust Prediction Model for Postoperative Pneumonia in Elderly Patients with Hip Fracture: Integrating Frailty, Nutrition, and Comprehensive Risk Assessment.","authors":"Liping Ma, Jiahong Tu, Yan Fu, Lan Guan, Zhaoxing Tian","doi":"10.2147/RMHP.S532215","DOIUrl":"10.2147/RMHP.S532215","url":null,"abstract":"<p><strong>Objective: </strong>To develop and rigorously validate a multivariable prediction model for postoperative pneumonia (POP) in elderly patients with hip fracture by integrating the five-item modified frailty index (5‑mFI), the Geriatric Nutritional Risk Index (GNRI), and various clinical variables, with comprehensive assessment of model performance.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study of 2183 patients aged ≥65 years undergoing hip fracture surgery. Predictors included comorbidities, laboratory values (including partial pressure of oxygen [PO₂], B-type natriuretic peptide [BNP], and GNRI), and the 5-mFI. We employed multivariable logistic regression to develop original and extended models, the latter adjusting for functional status and perioperative factors. Model performance was evaluated via area under the curve (AUC), bootstrap-corrected AUC, calibration, and decision curve analysis. Time-to-event and competing risk analyses were performed, and machine learning models (Random Forest, XGBoost) were compared.</p><p><strong>Results: </strong>The extended logistic regression model identified chronic obstructive pulmonary disease (odds ratio [OR]=2.60), postoperative intensive care unit admission (OR=2.72), lower PO₂ (OR=0.987), lower GNRI (OR=0.871), higher 5-mFI (OR=1.94), and higher BNP (OR=1.000) as independent predictors. The model demonstrated robust discrimination (AUC=0.781; bootstrap-corrected AUC=0.773), good calibration, and clinical utility. Results were consistent in competing risk analysis and robust to multiple imputation of missing data. Machine learning models confirmed GNRI and 5-mFI as top predictors, with comparable yet miscalibrated performance (XGBoost corrected AUC=0.791).</p><p><strong>Conclusion: </strong>We developed and internally validated a robust prediction model for POP that integrates frailty, nutrition, and key clinical variables. The model demonstrates strong, validated performance and clinical utility, providing a practical tool for preoperative risk stratification to guide targeted preventive measures in elderly patients with hip fracture.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"532215"},"PeriodicalIF":2.6,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13256095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stakeholder Perspectives on the Adaptability of Hospital Drug Formularies to Disease Patterns: A Modified Q-Methodology Study in Vietnam.","authors":"Mai-Hien Chuc, Thi-Thuan Tran, Hai-Anh Ha","doi":"10.2147/RMHP.S607810","DOIUrl":"10.2147/RMHP.S607810","url":null,"abstract":"<p><strong>Background: </strong>Hospital drug formularies function within Complex Adaptive Systems (CAS), where the alignment between drug supply and disease patterns is critical for patient care and cost-effectiveness. However, measuring this responsiveness is challenging due to conflicting stakeholder priorities and a lack of standardized assessment tools.</p><p><strong>Objective: </strong>This study aims to analyze the perspectives of three key stakeholder groups <b>-</b> clinical providers, non-clinical service providers, and policymakers <b>-</b> to develop a consensus-based set of indicators for monitoring the adaptability of drug formularies in Vietnam.</p><p><strong>Methods: </strong>We employed a modified Q-methodology approach involving 69 experts (20 non-clinical, 28 clinical, and 21 policymakers). A Q-set of 92 indicators across 9 criteria was developed. Participants evaluated these indicators using a 5-point Likert scale to determine levels of consensus and discordance, rather than a orced-distribution sort, to assess the absolute importance of adaptability metrics.</p><p><strong>Results: </strong>Most criteria achieved consensus with a Mean > 4.0, particularly for \"Usage\" and \"Storage\" adaptations. However, significant discordance was observed regarding \"Drug availability\", \"Procurement\", and \"Outputs and outcomes\". Notably, the correlation for \"Outputs and outcomes\" between non-clinical and policy stakeholders was statistically weak (r=0.09, p=0.356), highlighting a profound strategic gap.</p><p><strong>Conclusion: </strong>The study proposes a management framework that monitors drug list adaptability across four levels: non-adaptive, passive, active, and advanced adaptive. Success requires enhanced collaboration between specialized teams and a shift from efficiency-based to resilience-based performance metrics.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"607810"},"PeriodicalIF":2.6,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13245456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Artificial Intelligence Implementation in Routine Care: Real-World Operational Outcomes from a Provincial Health System in China.","authors":"Jin Tian, Yongzhao Song, Longmei Tang, Lejiao An, Jin Zhou","doi":"10.2147/RMHP.S604289","DOIUrl":"10.2147/RMHP.S604289","url":null,"abstract":"<p><strong>Background: </strong>Clinical artificial intelligence (AI) technologies are increasingly being introduced into hospital practice, yet evidence describing their operational integration and performance after deployment in routine clinical settings remains limited. This study examined the real-world implementation and operational integration of clinical AI within a provincial tertiary health system in China over an 18-month observation period.</p><p><strong>Methods: </strong>This retrospective longitudinal observational study used aggregated institutional data generated during routine platform deployment, including electronic medical record-linked system logs, deployment records, quality-monitoring summaries, and operational reports. The analysis focused on implementation patterns, workflow integration, selected operational indicators, and user acceptance during routine clinical use. The study evaluated implementation and operational integration rather than algorithmic accuracy, diagnostic performance, or patient-level clinical effectiveness.</p><p><strong>Results: </strong>Three AI-supported clinical pathways were included: an intelligent pre-consultation system, a multidisciplinary tumor decision-support system, and a duloxetine therapeutic drug-monitoring pathway. During the observation period, 127 clinicians across 53 specialties participated in AI-assisted clinical activities involving more than 27,000 patient encounters, 850 multidisciplinary tumor decision-support cases, and 320 therapeutic drug-monitoring episodes. Patient waiting time decreased from 18 to 13 minutes, patient satisfaction increased from 95.40% to 98.92%, consultation efficiency improved by approximately 40%, and documentation completion efficiency improved by approximately 80%. Implementation patterns differed substantially across pathways, reflecting differences in workflow position and clinical accountability rather than deployment effort alone.</p><p><strong>Conclusion: </strong>In this provincial tertiary health system, clinical AI implementation was associated with sustained operational use, cross-specialty workflow integration, and measurable changes in selected workflow indicators. These findings suggest that technical functionality alone is insufficient for sustained clinical AI use and that workflow compatibility and organizational readiness are central to routine implementation. Because this was a single-site observational study using aggregated operational data, the findings should be interpreted as implementation evidence rather than proof of clinical effectiveness.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"604289"},"PeriodicalIF":2.6,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13245460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Enhanced Recovery After Surgery as Patient Safety Strategies for Perioperative Care in Resource-Limited Settings.","authors":"Ibrahim Abdullahi Mohamed, Abdirahman Mohamed Hassan","doi":"10.2147/RMHP.S616788","DOIUrl":"10.2147/RMHP.S616788","url":null,"abstract":"<p><p>Artificial intelligence (AI) and Enhanced Recovery After Surgery (ERAS) are increasingly discussed as strategies for improving perioperative quality, efficiency, and recovery, but their implementation in resource limited settings requires a clear patient safety framework. In this commentary, resource limited settings refer to perioperative systems constrained by shortages of trained workforce, essential equipment, monitoring capacity, reliable data infrastructure, financing, or governance support. This commentary argues that AI and ERAS should be viewed as complementary rather than competing approaches. ERAS provides a structured pathway for standardizing perioperative care, while AI may support risk stratification, clinical decision support, monitoring, adherence tracking, and operational efficiency. Perioperative care is a suitable field for AI integration because it is time-sensitive, multidisciplinary, data-rich, and highly dependent on coordinated decisions across the preoperative, intraoperative, and postoperative continuum. However, digital tools cannot compensate for absent safety infrastructure, weak governance, or poor-quality data. A pragmatic strategy for low-resource settings is therefore to first establish essential perioperative safety standards, implement context-adapted ERAS elements, and then selectively deploy ethically governed AI applications with human oversight and local validation. Framed in this way, AI-supported ERAS pathways may strengthen patient safety, recovery, and risk management while supporting broader health system resilience.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"616788"},"PeriodicalIF":2.6,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13243354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consent Related Delay in Emergency Obstetric Care as a Sentinel Patient Safety Event: A Risk Governance Commentary from Somalia.","authors":"Yusuf Mohamed Salad, Nasteho Mohamud Mudei, Abdullahi Abdirahman Omar","doi":"10.2147/RMHP.S606125","DOIUrl":"10.2147/RMHP.S606125","url":null,"abstract":"<p><p>This evidence informed commentary addresses consent related delay in emergency obstetric care in Somalia, where clinician recommended escalation may be delayed by family mediated refusal, indecision, or uncertainty about emergency authority. The purpose is to strengthen the conceptual grounding and practical implementation of a risk governance response to this under recognized patient safety problem. The commentary draws on established patient safety concepts, maternal delay models, World Health Organization quality of care and near miss approaches, published evidence from Somali settings, and the Somali regulation on patient rights and responsibilities of healthcare workers. We propose an operational definition of consent related delay as a sentinel patient safety event when it contributes to serious maternal harm or creates substantial risk of such harm. The framework emphasizes timely recognition of contested consent, structured escalation, clear documentation, lawful emergency action, integration into maternal near miss and mortality review, and routine monitoring. This framing may support hospital managers, clinicians, and policymakers to convert a recurring source of avoidable delay into an auditable governance process while preserving respectful patient and family engagement.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"606125"},"PeriodicalIF":2.6,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13243358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tool for Predicting Disability Pension Applications in the Finnish Public Sector: Sickness Absences as Early Indicators of Declining Work Ability.","authors":"Petra Sohlman","doi":"10.2147/RMHP.S585895","DOIUrl":"10.2147/RMHP.S585895","url":null,"abstract":"<p><strong>Purpose: </strong>Identifying employees at the greatest risk of work disability is critical to preventing early exit from the labor market. Understanding how different factors influence work disability risk in the years leading to a pension is important in preventing early exits. Our aim in this study is to model the risk factors for an employee applying for a disability pension by using a novel and extensive register dataset and to evaluate the predicted risk for different occupations in the workforce.</p><p><strong>Methods: </strong>We use a logistic model to assess the risk of filing a first-time disability pension application using rich register data. We evaluate the performance of the model and finally present the evaluated risk distribution among current employees. Data are also included for short-term sickness absences, usually not available for register studies. A pension application is used as a follow-up to include employees whose application is rejected but who nonetheless carry a physician's note of diminished work ability, also a new approach to modeling work disability risk. The data cover all public sector occupations in Finland and their diverse disability risk profiles.</p><p><strong>Results: </strong>The constructed risk model accurately and transparently predicts the disability pension application risk in different employee groups. Age, occupation, cumulative sickness days, and number of sickness spells of all lengths, including short ones, were positively associated with the risk; proximity of retirement age and high earnings were negatively associated. Validation measures allow for using the model in providing employers with risk profiles of their employees.</p><p><strong>Conclusion: </strong>The developed model can be used as a tool for predicting the risk status of employee groups and for assessing the economic effects of sickness absences and disability pension risk. The modeling results reported here are already in practical use by public sector employers in Finland, eg, for early intervention.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"585895"},"PeriodicalIF":2.6,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148207160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of Glycemic Control Among Patients with Diabetes: A Cross-Sectional Study.","authors":"Salha Otaif, Ghareeb Bahari","doi":"10.2147/RMHP.S608364","DOIUrl":"10.2147/RMHP.S608364","url":null,"abstract":"<p><strong>Purpose: </strong>Poor glycemic control remains a major challenge and leads to complications among patients with diabetes. In this study, we aimed to identify strategies and factors contributing to improved glycemic control among patients with diabetes in Saudi Arabia.</p><p><strong>Patients and methods: </strong>This cross-sectional study used a convenience sample of 144 adults with diabetes who visited primary healthcare centers. Data were collected using a structured questionnaire that incorporated validated tools to assess multiple factors of glycemic control.</p><p><strong>Results: </strong>Among the 144 participants, only 41.6% achieved optimal HbA1c control, while 58.4% had suboptimal control, including 21.5% with very poor control. Physical activity levels were mostly low to moderate, and two-thirds of the participants reported poor medication adherence. Self-efficacy was moderate, with weaknesses in diet-related confidence. Physical activity was positively correlated with self-efficacy and negatively correlated with medication adherence. Similarly, self-efficacy was negatively associated with medication adherence. Glycemic control was weakly associated with these variables. Regression analysis identified duration since diagnosed with diabetes and medication adherence as significant factors of glycemic control, while self-efficacy and family support were strongly associated with physical activity.</p><p><strong>Conclusion: </strong>Glycemic control among patients with diabetes remains suboptimal. Targeted interventions that strengthen self-care, foster family support, and address behavioral barriers are essential for improving diabetes management and long-term health outcomes.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"19 ","pages":"608364"},"PeriodicalIF":2.6,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13225148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148152306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}