Major Accident Hazards – Own Your Barrier
Bibliographic record
Abstract
Abstract The Health and Safety Executive's analysis shows poor hazard identification and risk analysis is a causal factor in 12 out of 14 recent major hydrocarbon releases, demonstrating that major accidents could be prevented if workers had a better understanding of major accident hazards (MAHs). Therefore, it is proposed that improving awareness of MAHs across the workforce, both onshore and offshore, would lead to better MAH management and a reduction in major accidents. Once the domain of process engineers, major accident hazard management has been largely overlooked by much of industry. It was acknowledged as a problem but ignored in the hope that specialists had it under control. Step Change in Safety's Major Accident Hazard Understanding workgroup responded to this by identifying different job roles (onshore and offshore), evaluating the resources to develop MAH understanding already available and creating a suite of resources to fill the gaps. These resources include an e-learning tool for onshore (office-based) personnel, bowtie lunch and learn sessions, gap analysis tools to identify training requirements of offshore jobs, senior leaders' workshops and a MAH Awareness programme. The MAH Awareness programme, consisting of short films and presentations which can be customised to suit specific worksites and job roles. Each of the four packs explores different aspects of major accident management including MAH identification and analysis, bowties and safety and environmental critical elements, barrier maintenance, assurance and verification and the importance of taking responsibility of ‘owning’ your barrier. Analysis of questionnaires completed before and after exposure to the programme demonstrates that knowledge of MAH management increased by approximately 30%. Additionally, the data demonstrates that elected safety representatives have a greater base knowledge of MAHs than the general offshore workforce, as do technical staff compared to non-technical and those employed by operators compared to contractor employees. Whether this increased knowledge gained through taking part in the MAH Awareness programme is retained or impacts the number of major accidents has not yet been analysed but data such as the number of major accidents, including hydrocarbon releases, will be examined over forthcoming years to evaluate the effectiveness of the resources developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".