Artificial Intelligence-Based Prediction of Individual Differences in Psychological Occupational Therapy Intervention Guided by the Realization of Occupational Values.

PROFESSIONAL ethics; SUPPORT vector machines; RESEARCH; STATISTICS; ANALYSIS of variance; SAMPLE size (Statistics); ATTITUDE testing; MATHEMATICAL models; SELF-management (Psychology); TIME; ARTIFICIAL intelligence; MENTAL health; SOCIAL capital; COGNITION; HEALTH status indicators; OCCUPATIONAL therapy; JOB satisfaction; THEORY; PREDICTION models; ARTIFICIAL neural networks; MENTAL illness; PSYCHOTHERAPY; ALGORITHMS

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

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