Orrù Luisa, Cuccarini Marco, Moro Christian, Turchi Gian Piero
Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, 35122 Padova, Italy.
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy.
Behav Sci (Basel). 2024 Dec 19;14(12):1225. doi: 10.3390/bs14121225.
Despite their diverse assumptions, clinical psychology approaches share the goal of mental health promotion. The literature highlights their usefulness, but also some issues related to their effectiveness, such as their difficulties in monitoring psychological change. The elective strategy for activating and managing psychological change is the clinical question. But how do different types of questions foster psychological change? This work tries to answer this issue by studying therapist-patient interactions with a ML model for text analysis. The goal was to investigate how psychological change occurs thanks to different types of questions, and to see if the ML model recognized this difference in analyzing patients' answers to therapists' clinical questions. The experimental dataset of 14,567 texts was divided based on two different question purposes, splitting answers in two categories: those elicited by questions asking patients to start describing their clinical situation, or those from asking them to detail how they evaluate their situation and mental health condition. The hypothesis that these categories are distinguishable by the model was confirmed by the results, which corroborate the different valences of the questions. These results foreshadow the possibility to train ML and AI models to suggest clinical questions to therapists based on patients' answers, allowing the increase of clinicians' knowledge, techniques, and skills.
尽管临床心理学方法有着不同的假设，但它们都有促进心理健康的共同目标。文献强调了它们的有用性，但也指出了一些与有效性相关的问题，比如在监测心理变化方面存在困难。激活和管理心理变化的选择性策略是一个临床问题。但是不同类型的问题是如何促进心理变化的呢？这项工作试图通过使用文本分析的机器学习模型研究治疗师与患者的互动来回答这个问题。目标是研究由于不同类型的问题心理变化是如何发生的，并查看机器学习模型在分析患者对治疗师临床问题的回答时是否能识别这种差异。14567篇文本的实验数据集根据两种不同的问题目的进行了划分，将回答分为两类：一类是由要求患者开始描述其临床情况的问题引发的回答，另一类是由要求他们详细说明如何评估自己的情况和心理健康状况的问题引发的回答。模型能够区分这些类别的假设得到了结果的证实，这些结果证实了问题的不同效价。这些结果预示了训练机器学习和人工智能模型根据患者的回答为治疗师建议临床问题的可能性，从而增加临床医生的知识、技术和技能。
