Α Novel AI-based Modeling with Bias Classification Hybrid Risk Evaluation System for Confidence Enhanced Network Meta-Analysis of Occupational Hazards and Burnout Risk among Public Health Inspectors

Abstract

Public Health Inspectors (PHIs) serve a critical role in enforcing health and safety regulations, particularly under the growing pressures of climate change. With rising exposure to occupational hazards such as heat waves, air pollution, and vector-borne diseases, PHIs now face escalating stress and burnout. Geographical variability, limited resources, and institutional gaps in training and support further shape their complex risk profile. Despite growing concern, systematic, confidence-based evaluations of these occupational risks—especially tailored to PHIs—remain rare. This study addresses that gap using a Confidence in Network Meta-Analysis (CINeMA)-enhanced framework to assess domain-specific occupational risk profiles of PHIs working in climate-stressed environments. Drawing on an empirically collected dataset of emotional pressure, cognitive fatigue, organizational support, and environmental exposure, we conducted regression analyses and domain-level CINeMA confidence ratings. These included dimensions such as indirectness, imprecision, bias, and heterogeneity. Findings revealed moderate imprecision and within-study bias, with weak model fit suggesting latent variables beyond traditional exposure metrics. Semi-urban PHIs reported the highest climate-related impact scores (CCF mean= 2.91), while PhD-level PHIs showed lower susceptibility. We also propose a novel integration of AI-based topic modeling with CINeMA bias classification to support a future-ready hybrid risk evaluation system. Additionally, we introduce the Novel AI-based modeling Network Meta-Analysis, Adamopoulos–Valamontes Classification and Assessment Model (AV-CA Model)—a structured framework for classifying and assessing environmental, psychosocial, and organizational risks linked to climate impacts in PHI settings. These results support evidence-based OSH policy and training reforms that enhance the resilience of frontline public health systems amid escalating climate challenges.