Machine learning model distinguishes levels of psychological resilience in health care workers with 75% accuracy

by Universidad Tecnica Particular de Loja

edited by Sadie Harley, reviewed by Robert Egan

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A study published in Discover Artificial Intelligence used logistic regression, random forest and support vector machine (SVM) to classify health care workers according to their level of psychosocial resilience, using information collected during the COVID-19 pandemic. The logistic regression model produced the most accurate results, with an accuracy of 75.6% and an area under the ROC curve of 0.816, outperforming random forest (72.6%) and support vector machine (70.8%).

Unlike most studies on health care workers' mental health, which focus on burnout and other negative outcomes, this research sought to predict a person's ability to actively cope with stress.

To train the models, the team used the "How Right Now Mental Health & Coping" dataset, collected by NORC at the University of Chicago between 2021 and 2022, comprising 2,055 respondents in the United States. The researchers constructed a resilience index based on four psychometric variables—resilience, ability to bounce back, control and confidence—and divided it into two levels, high and low, using the median as the cutoff point.

The results showed that stress, with a weight of 0.182 in the model, depression (0.160) and anxiety (0.145) were the strongest predictors of low resilience, followed by hopelessness and changes in sleep.

Coping strategies—seeking social support, engaging in hobbies, prayer and meditation—had a lower weight in the model but were associated with greater resilience. Their effect appeared to be cumulative; in other words, respondents who reported using several strategies simultaneously had, on average, higher scores than those who used only one.

The authors noted that the data represent a single point in time, are based on participants' self-reports and come from a U.S. population. These factors limit the extent to which the conclusions can be applied to other health care systems, including those in Latin America.

They therefore proposed that the next step should be to validate the model with Latin American health care workers and incorporate other measures, such as sleep data or physiological indicators.

The authors also suggested that models of this kind, based on existing data, could be used by occupational health departments to identify people at risk at an early stage without relying on complex artificial intelligence systems.

More information

Pablo Torres-Carrion et al, Predicting psychosocial resilience in healthcare workers during COVID-19 using interpretable machine learning, Discover Artificial Intelligence (2026). DOI: 10.1007/s44163-026-01066-w

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PsychiatryPsychology & Mental healthOccupational medicine Provided by Universidad Tecnica Particular de Loja Who's behind this story?

Sadie Harley

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