Sikström Sverker, Nicolai Miriam, Ahrendt Josephine, Nevanlinna Suvi, Stille Lotta
Department of Psychology, Lund University, Lund, Sweden.
Npj Ment Health Res. 2024 Jul 31;3(1):37. doi: 10.1038/s44184-024-00080-z.
Rating scales are the dominating tool for the quantitative assessment of mental health. They are often believed to have a higher validity than language-based responses, which are the natural way of communicating mental states. Furthermore, it is unclear how difficulties articulating emotions-alexithymia-affect the accuracy of language-based communication of emotions. We investigated whether narratives describing emotional states are more accurately classified by questions-based computational analysis of language (QCLA) compared to commonly used rating scales. Additionally, we examined how this is affected by alexithymia. In Phase 1, participants (N = 348) generated narratives describing events related to depression, anxiety, satisfaction, and harmony. In Phase 2, another set of participants summarized the emotions described in the narratives of Phase 1 in five descriptive words and rating scales (PHQ-9, GAD-7, SWLS, and HILS). The words were quantified with a natural language processing model (i.e., LSA) and classified with machine learning (i.e., multinomial regression). The results showed that the language-based responses can be more accurate in classifying the emotional states compared to the rating scales. The degree of alexithymia did not influence the correctness of classification based on words or rating scales, suggesting that QCLA is not sensitive to alexithymia. However, narratives generated by people with high alexithymia were more difficult to classify than those generated by people with low alexithymia. These results suggest that the assessment of mental health may be improved by language-based responses analyzed by computational methods compared to currently used rating scales.
评定量表是心理健康定量评估的主要工具。人们通常认为它们比基于语言的反应具有更高的效度，而基于语言的反应是表达心理状态的自然方式。此外，尚不清楚表达情感困难（述情障碍）如何影响基于语言的情感交流的准确性。我们研究了与常用评定量表相比，基于问题的语言计算分析（QCLA）对描述情绪状态的叙述进行分类是否更准确。此外，我们还研究了这是如何受到述情障碍影响的。在第一阶段，参与者（N = 348）生成了描述与抑郁、焦虑、满意度和和谐相关事件的叙述。在第二阶段，另一组参与者用五个描述性词语和评定量表（PHQ - 9、GAD - 7、SWLS和HILS）总结了第一阶段叙述中描述的情绪。这些词语用自然语言处理模型（即LSA）进行量化，并用机器学习（即多项回归）进行分类。结果表明，与评定量表相比，基于语言的反应在对情绪状态进行分类时可能更准确。述情障碍的程度并不影响基于词语或评定量表的分类正确性，这表明QCLA对述情障碍不敏感。然而，高述情障碍者生成的叙述比低述情障碍者生成的叙述更难分类。这些结果表明，与目前使用的评定量表相比，通过计算方法分析基于语言的反应可能会改善心理健康评估。
