PREDICT PSYCHOSOCIAL RISK IN THE WORKPLACE THROUGH A BRIEF SURVEY APPLYING DATA ENGINEERING AND ARTIFICIAL NEURAL NETWORKS
DOI:
https://doi.org/10.18623/rvd.v23.6097Palabras clave:
Psychosocial Risk, Workplace Health, Artificial Neural Networks, Data Engineering, Employee Well-Being, Survey Methodology, Real-Time Assessment, Intergenerational Workplace, NOM-035Resumen
This study presents a novel methodology for automating the assessment of psychosocial risks in the workplace using Artificial Neural Networks (ANN) and a brief digital survey. By leveraging data engineering techniques—including k-means clustering, Random Forest (RF) feature selection, and dimensionality reduction—the developed framework enables real-time evaluation of employee risk levels, delivering immediate feedback through a traffic light system indicating low, moderate, or high risk. Applied to a dataset of 416 records drawn from the official Mexican NOM-035-STPS-2018 standard questionnaire, the ANN model achieved an overall accuracy of 84.34% (κ = 0.7638) and was externally validated with 83.9% accuracy on an independent sample. Such proactive detection facilitates timely organizational interventions aimed at enhancing employee well-being and productivity. The study underscores the significance of leadership quality and performance recognition as key psychosocial dimensions, particularly in light of the evolving workforce dynamics involving Millennials and Generation Z, who increasingly prioritize personal development, ethical values, and meaningful professional relationships over material compensation.
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