Machine Learning Based Prediction of Burnout in Nurses During the COVID-19 Pandemic - Wei Chen Yeu; Na Zu Yin (Safety and Health for Medical Workers, 2025)
Objective: The study looks at what causes nurse burnout and the part played by resilience and the support offered by the organisation in healthcare settings.Methods: A predictive burnout model was developed. This was done using machine learning and structural equation modelling. The aim was to analyse data collected through validated psychometric instruments.Findings: The predominant predictors of burnout have been identified as workload, psychological stress and extended shift duration. Resilience plays a key role as a mediator, helping to understand how these risk factors lead to symptoms of burnout. Organisational support is crucial in this respect, as it has been found to have a buffering effect that significantly reduces the negative impact of job demands. The model that is part of the study gets the results right most of the time when it comes to working out the risk of someone burning out, which shows that using a mix of machine learning and theories is a good idea.Novelty: This research presents a new way of doing things by combining machine learning predictive analytics with well-known psychological theories to create a complete assessment framework for burnout. It provides new information about the way in which the strength of individuals and the support they get from their organisations can influence the process of "burnout".Research Implications: The findings support interventions at two levels: individual resilience training combined with organisational support systems. People who run hospitals should introduce ways to predict and prevent problems, and support programs that look at the psychological needs of workers and the way work is organised. This will help to stop people from becoming exhausted and stressed.