Arnout Boshra A, Alshehri Sami M
Department of Psychology, College of Education, King Khalid University, P.O. Box 2380, Abha 62521, Saudi Arabia.
Department of Psychology, College of Arts, Zagazig University, Zagazig 44511, Egypt.
Behav Sci (Basel). 2024 Dec 29;15(1):21. doi: 10.3390/bs15010021.
Despite the significant increase in studies on AI applications in many aspects of life, its applications in mental health services still require further studies. This study aimed to test a proposed structural model of the relationships between AI use, therapeutic alliance, and job engagement by PLS-SEM. The descriptive method was applied. The sample consisted of (382) mental health service providers in Saudi Arabia, including 178 men and 204 women between 25 and 50 (36.32 ± 6.43) years old. The Artificial Intelligence Questionnaire, the Therapeutic Alliance Scale, and the Job Engagement Scale were applied in this study. The results showed the structural model's predictability for using AI and the therapeutic alliance in predicting job engagement and explaining the causal relationships between them compared to the indicator average and linear models. The study also found a strong positive overall statistically significant effect ( < 0.05) of the use of AI on therapeutic alliance (0.941) and job engagement (0.930) and a positive overall average statistically significant effect ( < 0.05) of the therapeutic alliance on job engagement (0.694). These findings indicated the importance of integrating AI applications and therapeutic alliance skills into training and professional development plans.
尽管人工智能在生活诸多方面的应用研究显著增加，但其在心理健康服务中的应用仍需进一步研究。本研究旨在通过偏最小二乘结构方程模型（PLS-SEM）检验所提出的人工智能使用、治疗联盟和工作投入之间关系的结构模型。采用描述性方法。样本包括沙特阿拉伯的382名心理健康服务提供者，其中178名男性和204名女性，年龄在25至50岁之间（平均36.32 ± 6.43岁）。本研究使用了人工智能问卷、治疗联盟量表和工作投入量表。结果表明，与指标平均模型和线性模型相比，该结构模型在使用人工智能和治疗联盟预测工作投入以及解释它们之间的因果关系方面具有可预测性。研究还发现，人工智能的使用对治疗联盟（0.941）和工作投入（0.930）具有总体上强的正向统计显著效应（<0.05），治疗联盟对工作投入（0.694）具有总体上正向的平均统计显著效应（<0.05）。这些发现表明将人工智能应用和治疗联盟技能纳入培训和专业发展计划的重要性。
