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The Prevention of Occupational Injuries in 2 industrial plants using an Incident Reporting Scheme

Background. Accident or incident analysis is used in proactive accident prevention models based on different theories of accident causation Ex. Safety iceberg" assumption. Objectives: . To introduce an in-depth analysis tool for accidents and near-misses in 2 industrial plants To investigate

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The Prevention of Occupational Injuries in 2 industrial plants using an Incident Reporting Scheme

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    1. Ole Carstensen Kent Jacob Nielsen Kurt Rasmussen The Prevention of Occupational Injuries in 2 industrial plants using an Incident Reporting Scheme

    2. Background Accident or incident analysis is used in proactive accident prevention models based on different theories of accident causation Ex. “Safety iceberg” assumption

    3. Objectives: To introduce an in-depth analysis tool for accidents and near-misses in 2 industrial plants To investigate whether a higher degree of registration and in-depth analysis of minor accidents (MI) and near-misses (NS) will have a preventive effect on the total number of lost time injuries (LTI) in the plants during a 2-year period. To investigate promoting and inhibiting factors for using an in-depth analysis and registration system in the companies

    4. Time Schedule

    5. Intervention New registration system Registration of Minor accidents and near-misses Analyses of the accidents and incidents to discover problem areas and taking preventive measures.

    6. Effect measurement: Analysis of accident data from 1 year before study start to 2 years after study start. Questionnaire at baseline and 2 years later Safety climate, Management’s commitment to safety Interviews (with employees) at baseline and 2 years later Safety practices Accident registration Occupational accidents Safety attitudes

    7. Material and method

    10. Results: Injury rates

    11. Results: Injury rates

    12. Results: Injury rates

    13. Results: Questionnaire data Safety Climate

    14. Results: Questionnaire data Managements commitment to safety

    16. Results: Interviews

    17. Conclusion:

    18. Conclusion:

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