Cross-sectional survey reveals high hazard environments more than double accident probabilities in mine workers, highlighting the need for proactive safety systems.
Key Points
To assess current occupational health and safety practices and develop a probabilistic model capturing dependencies among workplace hazards, preventative actions, and accident occurrences.
Surveyed 298 mine workers, safety officers, and supervisors in Ghana using a quantitative analytical design.
Extracted latent safety factors using Principal Component Analysis and categorized worker safety profiles via K-modes and Hierarchical Clustering.
Constructed a Bayesian Network model to calculate conditional probabilities linking hazards, health issues, and accident rates.
Identified three primary safety dimensions from Principal Component Analysis: perceived adequacy of safety measures, formal training exposure, and safety resources.
Demonstrated that high hazard environments increase the probability of an accident from 0.216 to 0.453, more than doubling baseline accident risk.
Found conditional dependencies between reported health symptoms and formal accident occurrences, showing that physical equipment provision does not align with perceived operational safety.