Which Human Reliability Analysis Methods Are Most used in Industrial Practice? -- A Preliminary Systematic Review
Bibliographic record
Abstract
Human reliability analysis (HRA) is the most acknowledged methodology to assess the probability of human errors depending on the tasks and its contextual factors.There are many methods available, but some exploratory research show that only a few of them are frequently cited in research papers, or even accepted by safety regulators.This paper describes the methodology used to do the systematic review approach to understand which HRA techniques are the most cited by country and by industry sector along the years.The research methodology has considered only research papers.The results per country focus only on the oil & gas industry sector, more specifically on the countries with safety regulators which are part of the International Offshore Forum (IRF): Australia, Brazil, Canada, Denmark, Ireland, Mexico, The Netherlands, New Zealand, Norway, United Kingdom, and United States of America.Future development of this review is to also review regulations and consultancy companies' portfolios.The aim is to understand in which level the industrial practice follows the pattern observed in academia and if they are influenced by regulations.
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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 | 0.127 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".