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...

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Autores principales:	Masahiro Nishi, Michiyo Yamano, Satoaki Matoba
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4d78776113ff4eb385feffe0a4497c0b
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spelling	oai:doaj.org-article:4d78776113ff4eb385feffe0a4497c0b2021-12-02T20:09:10ZPrediction of well-being and insight into work-life integration among physicians using machine learning approach.1932-620310.1371/journal.pone.0254795https://doaj.org/article/4d78776113ff4eb385feffe0a4497c0b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254795https://doaj.org/toc/1932-6203There 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.Masahiro NishiMichiyo YamanoSatoaki MatobaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254795 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Masahiro Nishi Michiyo Yamano Satoaki Matoba Prediction of well-being and insight into work-life integration among physicians using machine learning approach.
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.
format	article
author	Masahiro Nishi Michiyo Yamano Satoaki Matoba
author_facet	Masahiro Nishi Michiyo Yamano Satoaki Matoba
author_sort	Masahiro Nishi
title	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_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_sort	prediction of well-being and insight into work-life integration among physicians using machine learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4d78776113ff4eb385feffe0a4497c0b
work_keys_str_mv	AT masahironishi predictionofwellbeingandinsightintoworklifeintegrationamongphysiciansusingmachinelearningapproach AT michiyoyamano predictionofwellbeingandinsightintoworklifeintegrationamongphysiciansusingmachinelearningapproach AT satoakimatoba predictionofwellbeingandinsightintoworklifeintegrationamongphysiciansusingmachinelearningapproach
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Prediction of well-being and insight into work-life integration among physicians using machine learning approach.

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