Abstract
During the outbreak of coronavirus disease 2019, psychological hotline counselors frequently address help-seekers’ traumatic experiences from time to time, which possibly causes counselors’ compassion fatigue. The present study aimed to explore the predictors of compassion fatigue among a high-risk population of psychological hotline counselors. Seven hundred and twelve psychological hotline counselors were recruited from the Mental Health Service Platform at Central China Normal University, Ministry of Education, then were asked to complete the questionnaires measuring compassion fatigue, trait empathy, social support, trait mindfulness, counselor’s self-efficacy, humor, life meaning, and post-traumatic growth. A chi-square test was utilized to filter for the top-20 predictive variables. Machine learning techniques, including logistic regression, decision tree, random forest, k-nearest neighbor, support vector machine, and naïve Bayes were employed to predict compassion fatigue. The results showed that the most important predictors of compassion fatigue were meaning in life, counselors’ self-efficacy, mindfulness, and empathy. Except for the decision tree, the rest machine learning techniques obtained good performance. Naïve Bayes presented the highest area under the receiver operating characteristic curve of 0.803. Random forest achieved the least classification error of 23.64, followed by Naïve Bayes with a classification error of 23.85. These findings support the potential application of machine learning techniques in the prediction of compassion fatigue.
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The raw data of the present study are available from the corresponding author on reasonable request.
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Acknowledgments
Our sincere thanks go to Lizu Lai, Yifei Yan, Chunxiao Zhao, and Mei Luo for data collecting. Thanks also to the Mental Health Service Platform at Central China Normal University, Ministry of Education for providing the data for the study.
Funding
This research was supported by the National Social Science Foundation of China (16ZDA232) and self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (CCNU20TD001).
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LZ: conceptualization, data analysis, and original draft writing. TZ: original draft writing. ZHR: funding acquisition and review. GRJ: funding acquisition and review. All authors contributed to the article and approved the submitted version.
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The research involving human participants was reviewed and approved by the Ethical Committee for Scientific Research of Central China Normal University. The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
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Zhang, L., Zhang, T., Ren, Z. et al. Predicting compassion fatigue among psychological hotline counselors using machine learning techniques. Curr Psychol 42, 4169–4180 (2023). https://doi.org/10.1007/s12144-021-01776-7
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DOI: https://doi.org/10.1007/s12144-021-01776-7
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