Using an explainable machine learning approach to prioritize factors contributing to healthcare professionals’ burnout-Reference-Cited by-同舟云学术

Using an explainable machine learning approach to prioritize factors contributing to healthcare professionals’ burnout

Published:2024-06-12 Issue:4 Volume:62 Page:1113-1124
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Pillai Malvika^ORCID,Liu Chao Chin,Kwong Elizabeth,Kratzke Ian,Charguia Nadia,Mazur Lukasz,Adapa Karthik^ORCID

Funder

Veterans Affairs Big Data-Scientist Training Enhancement Program

UNC Health Wellbeing Program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10844-024-00862-z.pdf

Reference16 articles.

1. De Hert, S. (2020). Burnout in healthcare workers: Prevalence, impact and preventative strategies. Local and Regional Anesthesia, 13, 171–183. https://doi.org/10.2147/LRA.S240564

2. Edwards, S. T., Helfrich, C. D., Grembowski, D., Hulen, E., Clinton, W. L., Wood, G. B., et al. (2018). Task delegation and burnout trade-offs among primary care providers and nurses in Veterans Affairs Patient Aligned Care Teams (VA PACTs). The Journal of the American Board of Family Medicine., 31(1), 83–93.

3. Guardian, T. (2021) Lost on the Frontline: US Healthcare Workers Who Died Fighting Covid-19. Retrieved January from https://www.theguardian.com/us-news/ng-interactive.

4. Helfrich, C. D., Simonetti, J. A., Clinton, W. L., Wood, G. B., Taylor, L., Schectman, G., et al. (2017). The association of team-specific workload and staffing with odds of burnout among VA primary care team members. Journal of General Internal Medicine., 32, 760–766.

5. Kurbatov, V., Shaughnessy, M., Baratta, V., Heller, D. R., Freedman-Weiss, M., Resio, B. J., et al. (2020). Application of advanced bioinformatics to understand and predict burnout among surgical trainees. Journal of Surgical Education., 77(3), 499–507.

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