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10.1007/s12144-021-01776-7 http://scihub22266oqcxt.onion/10.1007/s12144-021-01776-7 33935474!8074269!33935474     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Psychol 2023 ; 42 (5): 4169-4180 Nephropedia Template TP {{tp|p=33935474|t=2023. Predicting compassion fatigue among psychological hotline counselors using machine learning techniques |pdf=|usr=}}{{33935474}} gab.com Text Predicting compassion fatigue among psychological hotline counselors using machine learning techniques JO: Curr Psychol http://www.kidney.de/pget.php?&tw=1&pmid=33935474 #FOAvip 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 naive 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. Naive 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 Naive Bayes with a classification error of 23.85. These findings support the potential application of machine learning techniques in the prediction of compassion fatigue. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12144-021-01776-7. Twit Text FOAVip Predicting compassion fatigue among psychological hotline counselors using machine learning techniques ????Curr Psychol http://www.kidney.de/pget.php?&tw=1&pmid=33935474 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33935474 #FOAvip English Wikipedia <ref>{{Cite pmid|33935474}}</ref> Predicting compassion fatigue among psychological hotline counselors using machine learning techniques #MMPMID33935474 Zhang L; Zhang T; Ren Z; Jiang G Curr Psychol 2023[]; 42 (5): 4169-4180 PMID33935474show ga 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 naive 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. Naive 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 Naive Bayes with a classification error of 23.85. These findings support the potential application of machine learning techniques in the prediction of compassion fatigue. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12144-021-01776-7. äPredicting compassion fatigue among psychological hotline counselors u(epmc).pdf DeepDyve Pubget Overpricing