ArticleBehavioral sciences (Basel, Switzerland)2025
A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11).
Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The Chinese version of the Symptom Checklist-90 (SCL-90) is excessively lengthy, resulting in extended completion time and reduced respondent compliance. This study aimed to utilize a condensed subset of items from the Chinese SCL-90 to identify individuals at high risk for psychological disorders based on machine learning methods, forming a concise and efficient preliminary psychopathological screening instrument for the Chinese general population. Analyzing data collected from 4808 SCL-90 psychological surveys, this study applied variable clustering to select the most representative items, resulting in an 11-item scale: the Chinese Symptom Checklist-11 (CSCL-11). The CSCL-11 demonstrated high internal consistency (Cronbach's α = 0.84). The results of factor analysis supported a single-factor model for the CSCL-11, demonstrating an acceptable fit (SRMR = 0.035, RMSEA = 0.064, CFI = 0.935, and TLI = 0.919). The CSCL-11 demonstrated strong predictive performance for the Global Severity Index (GSI; RMSE = 0.11, R
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