Prediction of well-being and insight into work-life integration among physicians using machine learning approach.

ABSTRACT: There has been increasing interest in examining physician well-being and its predictive factors. However, few studies have revealed the characteristics associated with physician well-being and work-life integration using a machine learning approach. To investigate predictive factors of well-being and obtain insights into work-life integration, the survey was conducted by letter mail in a sample of Japanese physicians. A total of 422 responses were collected from 846 physicians. The mean age was 47.9 years, males constituted 83.3% of the physicians, and 88.6% were considered to be well. The most accurate machine learning model showed a mean area under the curve of 0.72. The mean permutation importance of career satisfaction, work hours per week, existence of family support, gender, and exis

SUBMITTER: Nishi M 

PROVIDER: S-EPMC8282024 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature