Prediction of well-being and insight into work-life integration among physicians using machine learning approach.
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 an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0254795 |
| _version_ | 1818720869368725504 |
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| author | Masahiro Nishi Michiyo Yamano Satoaki Matoba |
| author_facet | Masahiro Nishi Michiyo Yamano Satoaki Matoba |
| author_sort | Masahiro Nishi |
| collection | DOAJ |
| description | 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 existence of power harassment were 0.057, 0.022, 0.009, 0.01, and 0.006, respectively. Using a machine learning model, physician well-being could be predicted. It seems to be influenced by multiple factors, such as career satisfaction, work hours per week, family support, gender, and power harassment. Career satisfaction has the highest impact, while long work hours have a negative effect on well-being. These findings support the need for organizational interventions to promote physician well-being and improve the quality of medical care. |
| first_indexed | 2024-12-17T20:29:41Z |
| format | Article |
| id | doaj.art-4d78776113ff4eb385feffe0a4497c0b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T20:29:41Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4d78776113ff4eb385feffe0a4497c0b2022-12-21T21:33:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025479510.1371/journal.pone.0254795Prediction of well-being and insight into work-life integration among physicians using machine learning approach.Masahiro NishiMichiyo YamanoSatoaki MatobaThere 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 existence of power harassment were 0.057, 0.022, 0.009, 0.01, and 0.006, respectively. Using a machine learning model, physician well-being could be predicted. It seems to be influenced by multiple factors, such as career satisfaction, work hours per week, family support, gender, and power harassment. Career satisfaction has the highest impact, while long work hours have a negative effect on well-being. These findings support the need for organizational interventions to promote physician well-being and improve the quality of medical care.https://doi.org/10.1371/journal.pone.0254795 |
| spellingShingle | Masahiro Nishi Michiyo Yamano Satoaki Matoba Prediction of well-being and insight into work-life integration among physicians using machine learning approach. PLoS ONE |
| title | Prediction of well-being and insight into work-life integration among physicians using machine learning approach. |
| title_full | Prediction of well-being and insight into work-life integration among physicians using machine learning approach. |
| title_fullStr | Prediction of well-being and insight into work-life integration among physicians using machine learning approach. |
| title_full_unstemmed | Prediction of well-being and insight into work-life integration among physicians using machine learning approach. |
| title_short | Prediction of well-being and insight into work-life integration among physicians using machine learning approach. |
| title_sort | prediction of well being and insight into work life integration among physicians using machine learning approach |
| url | https://doi.org/10.1371/journal.pone.0254795 |
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