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Machine learning predicts psychosocial resilience among healthcare workers (opens in a new tab)

news-medical.net · 2026-09-18

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Predicting psychosocial resilience in healthcare workers during COVID-19 using interpretable machine learning

Discover Artificial Intelligence · 2026
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Claim 1 of 8Not checkedResearchers trained logistic regression, random forest, and support vector machine models to classify healthcare workers by psychosocial resilience using data collected during the COVID-19 pandemic.View evidenceHide evidence

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Claim 2 of 8Not checkedThe logistic regression model performed best, with 75.6% accuracy and an area under the ROC curve of 0.816, outperforming random forest (72.6%) and support vector machine (70.8%).View evidenceHide evidence

As stated75.6% accuracy; AUC 0.816; 72.6% and 70.8% for the other models

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Claim 3 of 8Not checkedThe study used the How Right Now Mental Health & Coping dataset from NORC at the University of Chicago, collected between 2021 and 2022 and including 2,055 U.S. respondents.View evidenceHide evidence

As stated2,055 respondents

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Claim 4 of 8Not checkedThe researchers built a resilience index from resilience, ability to bounce back, control, and confidence, then split it into high and low groups using the median cutoff.View evidenceHide evidence

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Claim 5 of 8Not checkedStress, depression, and anxiety were the strongest predictors of low resilience, while hopelessness and changes in sleep also contributed.View evidenceHide evidence

As statedstress 0.182; depression 0.160; anxiety 0.145

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Claim 6 of 8Not checkedCoping strategies such as seeking social support, engaging in hobbies, prayer, and meditation were associated with greater resilience, and the effect appeared cumulative when several strategies were used together.View evidenceHide evidence

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Claim 7 of 8Not checkedThe authors said the data were cross-sectional, self-reported, and drawn from a U.S. population, limiting generalizability to other healthcare systems, including Latin America.View evidenceHide evidence

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Claim 8 of 8Not checkedThe authors suggested validating the model in Latin American healthcare workers and adding measures such as sleep data or physiological indicators, and proposed that occupational health departments could use similar models for early identification of at-risk workers.View evidenceHide evidence

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Predicting psychosocial resilience in healthcare workers during COVID-19 using interpretable machine learning

Discover Artificial Intelligence · 2026

Source record (opens in a new tab)

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Crossref, PubMed, Europe PMC · 16 candidate papers

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Predicting psychosocial resilience in healthcare workers during COVID-19 using interpretable machine learning

Discover Artificial Intelligence · 2026 · Crossref

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