Predicting nurses' intention to quit with a support vector machine: a new approach to set up an early warning mechanism in human resource management.
This project developed a Support Vector Machine for predicting nurses' intention to quit, using working motivation, job satisfaction, and stress levels as predictors. This study was conducted in three hospitals located in southern Taiwan. The target population was all nurses (389 valid cases). For cross-validation, we randomly split cases into four groups of approximately equal sizes, and performed four training runs. After the training, the average percentage of misclassification on the training data was 0.86, while that on the testing data was 10.8, resulting in predictions with 89.2% accuracy. This Support Vector Machine can predict nurses' intention to quit, without asking these nurses whether they have an intention to quit.
Tzeng
Huey-Ming
HM
Department of Nursing, I-Shou University, Kaohsiung County, Kaohsiung, Taiwan, ROC. tzeng_hueyming@yahoo.com
Hsieh
Jer-Guang
JG
Lin
Yih-Lon
YL
D000328:Adult; D001291:Attitude of Health Personnel; D015982:Bias; D002055:Burnout, Professional / Q000453:epidemiology / Q000523:psychology; D006801:Humans; D033182:Intention; D007588:Job Satisfaction; D016015:Logistic Models; D008875:Middle Aged; D008960:Models, Psychological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015401:Nursing Administration Research; D009741:Nursing Staff, Hospital / Q000458:organization & administration / Q000523:psychology*; D010559:Personnel Management; D010563:Personnel Turnover / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012307:Risk Factors; D011795:Surveys and Questionnaires; D013624:Taiwan / Q000453:epidemiology; D017132:Workplace / Q000458:organization & administration / Q000523:psychology