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Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan
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Department of Uzbek Language and Literature, Bukhara State Pedagogical Institute Bukhara, Uzbekistan
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Professor, Department of Theory and Methodology of Primary Education, Jizzakh State Pedagogical University, Jizzakh, Uzbekistan
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National Institute of Educational Pedagogy named after Qori Niyoziy, Tashkent, Uzbekistan
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National Pedagogical University of Uzbekistan named after Nizami of Uzbekistan, Tashkent, Uzbekistan
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Uzbekistan State World Languages University, Tashkent, Uzbekistan
Associate Professor, Department of Psychology, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan
It is still very hard to predict addiction rehabilitation outcomes at the time of admission since current methods of evaluation do not recognize the interconnection between an individual's biomedical state and personality functioning. In this paper, Introduce Biomedical Assessment Framework for Evaluating Adaptive Personality Potential (BAF-APP), which allows integrating physiological, psychometric, clinical, and psychosocial measures into one composite measure to use for stratification of the individual's risk of failure during the process of rehabilitation at the time of admission. Adaptive Personality Potential means an individual's remaining behavioral and dispositional resources to reorganize his/her expression of maladaptive traits, such as neuroticism, disinhibition, and negative emotionality in particular, to achieve more regulated and treatment-compatible behavior during structured intervention. The framework consists of four input modules (biomedical, personality, clinical-behavioral, and psychosocial) and uses harmonization engine to create Adaptive Personality Potential Index (APPI). Then, the APPI is passed through a predictive analytics module to use for stratification of relapse risk, forecasting of outcomes, and personalized planning of intervention. This framework is implemented using a sequential protocol consisting of nine stages over twelve weeks. Based on multiple streams of convergent evidence derived from personality trait recovery, neurobiology of addiction, machine learning-based studies on relapse prediction, and resilience science, the manuscript argues that low neuroticism, high conscientiousness, and effective coping with stress are converging predictors of successful rehabilitation. Relevant analyses show how the resulting composite score discriminates between trajectories in baseline tertiles and correlates with the period of abstinence. Emphasize how there is a robust correlation between architecture and benchmark of abstinence duration for 12 months (r≈0.71), thus creating a coherent pattern to address fragmentary methods of intake in addiction medicine. While not meant to be used as a diagnostic test, the model is intended to provide a repeatable protocol for assessing personality adaptability during rehabilitation, complementing clinical decisions but not replacing them. Its main value lies in providing a reusable template for operationalizing personality adaptability within regular biomedical rehabilitation protocols.
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