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Updated: Jun 29, 2025
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Published on: February 12, 2015
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[Study on causative model of acute occupational poisoning accidents based on interpretative structural model-Bayesian
Summary
Preventing acute occupational poisoning requires addressing key factors like protective measures, equipment, and emergency management. Strengthening safety systems and training is crucial for reducing accident incidence.
Area of Science:
- Occupational Health and Safety
- Toxicology
- Risk Management
Context:
- Acute occupational poisoning accidents pose significant risks in various industries.
- Understanding the complex causal factors is essential for effective prevention strategies.
Purpose:
- To identify and analyze the causal factors of acute occupational poisoning accidents.
- To develop a predictive model for accident prevention using Interpretative Structural Modeling (ISM) and Bayesian Networks (BN).
Summary:
- A study of 232 acute occupational poisoning cases (2013-2022) utilized ISM to establish a hierarchical model of causal factors.
- Bayesian Network analysis identified the causal chain: safety responsibility system → enterprise supervision → safety training → protective measures → accident occurrence.
- Key contributing factors include inadequate protective measures, equipment failures, operational errors, ventilation issues, and improper emergency management.
Impact:
- The findings provide a scientific basis for targeted interventions to prevent acute occupational poisoning.
- Recommendations focus on correct use of protective measures, equipment checks, regulatory compliance, ventilation, and enhanced emergency management.
- Implementing these measures can significantly reduce the incidence of acute occupational poisoning accidents.
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