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All procedures adhered to the ethical standards outlined in the Declaration of Helsinki.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Written informed consent was obtained from all participants prior to their inclusion in the study. Participants were informed of the study\u2019s purpose, procedures, and their right to withdraw at any time without affecting their medical care.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The General Alienation Scale (GAS) was used under fair academic use principles. Formal permission for its use was not required as the scale is widely recognized in published literature and its application aligns with non-commercial research purposes. All other referenced materials are cited appropriately, and no copyrighted figures or tables were reproduced.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Permission to reproduce material from other sources"}},{"value":"This study is a cross-sectional observational analysis and does not involve clinical trials. Therefore, clinical trial registration was not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial registration"}},{"value":"This study makes significant contributions to both theoretical and practical domains in the field of nursing and chronic disease management through the following aspects:Theoretical advancementsIdentification of key risk factors: By employing the Apriori algorithm, this research systematically uncovers seven strong association rules related to social alienation in maintenance hemodialysis (MHD) patients, revealing multi-dimensional interactions among variables such as medical insurance type (e.g., urban employee basic insurance), dialysis frequency (three sessions per week), age (36\u201350\u00a0years), and educational level (senior high school\/technical secondary school). These findings fill a critical gap in understanding the complex drivers of social isolation in this vulnerable population.Integration of social support theory: The study bridges the gap between data-driven insights and social support frameworks, proposing a novel conceptual model that links \"time-role deprivation\" to social alienation, thereby enriching theoretical discussions on chronic disease management.Methodological innovationApplication of Apriori algorithm in nursing research: This work pioneers the use of association rule mining (ARM) in analyzing psychosocial outcomes among MHD patients, demonstrating the algorithm\u2019s efficacy in identifying hidden patterns within clinical datasets. The methodological approach provides a replicable framework for future studies aiming to explore multifactorial health behaviors or psychological states.Practical implicationsTargeted intervention strategies: The study proposes evidence-based, hierarchical interventions tailored to high-risk subgroups, such as flexible dialysis scheduling for employed patients, community-based support networks, and AI-driven psychological monitoring. These strategies address both systemic barriers (e.g., treatment time conflicts) and individual-level challenges (e.g., stigma management).Policy recommendations: By highlighting the role of medical insurance systems and socioeconomic factors, the findings advocate for policy reforms to optimize resource allocation and reduce disparities in care access for MHD patients.Social impactEnhancing patient-centered care: The research underscores the necessity of transitioning from a disease-focused to a holistic, patient-centered care model. By mitigating social alienation, the proposed interventions aim to improve patients\u2019 quality of life, mental health, and societal reintegration.Empowering multidisciplinary collaboration: The study advocates for integrating nursing, psychology, and health informatics to address the psychosocial complexities of chronic disease management, setting a precedent for cross-disciplinary collaboration in clinical practice.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Contribution of the paper"}},{"value":"Digital health solutions: The paper introduces innovative applications of technology, such as AI-based risk prediction models and virtual reality-assisted social interaction platforms, offering scalable tools to support long-term patient engagement and self-management.","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Technological integration"}}]}}