ArticleEuropean journal of investigation in health, psychology and education2025
Predicting Workplace Hazard, Stress and Burnout Among Public Health Inspectors: An AI-Driven Analysis in the Context of Climate Change.
Article in European journal of investigation in health, psychology and education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Climate Change Impacts on One Health Systems: A Narrative Review of Empirical Evidence and Policy Responses.Public health challenges · 2026Review
- Occupational resilience: Scoping review of conceptualisations and applications in contexts of adversity.Australian occupational therapy journal · 2026Article
- Machine learning in the analysis of mental health at work: a scoping review.Journal of occupational health · 2026Article
- Machine learning-based detection of workplace stress using wearable and multimodal data: a systematic literature review.Frontiers in artificial intelligence · 2026Review
- Machine learning analysis of the association between psychosocial risks and burnout syndrome in mining workers.Frontiers in public health · 2026Article
- A machine learning approach to behavioral signals of burnout in audit and control professions.Frontiers in psychology · 2026Article
- Determining the direction of pro-safety activities using selected methods of statistical analysis.Frontiers in public health · 2026Article
- Personalized AI for workplace health promotion: performance management and healthcare worker engagement through digital analytics.Frontiers in public health · 2025Review
- Work-Life Conditions as the Primary Determinant of Seafarer Mental Health: An Explainable Machine Learning Analysis.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85-90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.