Safety Culture Indicators - For Improvement Not Assessment
Bibliographic record
Abstract
This paper presents the findings of an evidence-based review of safety culture indicators and their applicability to the railway industry. Safety culture continues to be a major area of interest for railway companies in many countries. Much of the research focus has been on the development and evaluation of assessment methodologies. More recently many railway regulators have produced guidance on safety culture (e.g., EU Railway Safety Agency, Transport Canada). Some regulators are also incorporating safety culture into their oversight activities. For example, in the UK the Office of Road and Rail includes culture in its RM3 process and in the USA the Federal Railroad Administration has conducted a supplemental safety audit of Norfolk Southern to assess its overall safety culture. There is now an interest from both companies and regulators to use safety culture indicators. To identify potential safety culture indicators, an environmental scan was conducted to identify existing safety culture indicators. We identified 154 safety culture indicators from a range of sources (e.g., RM3, Railway Association of Canada, Canada Energy Regulator). These indicators varied widely in how they were developed, their intended purpose, and their target industry. The second phase of research involved interviewing eight subject matter experts (SMEs) to create an evidenced-based framework for evaluating the indicators. We used thematic analysis to identify three criteria of importance. Firstly, indicators need to be related to safety culture, secondly, practical/collectable, and thirdly they need clear assessment criteria. Using these criteria, we refined the original list of indicators in two phases by getting SMEs to rate the indicators. Two separate groups of six SMEs rated the indicators. The indicators that had low scores on these criteria were removed. We retained 27 indicators after two independent rounds of assessment. This research reveals that many safety culture indicators have been created with limited or no evaluation. The fact that we only retained 27, questions the quality of many of these indicators. These indicators can only provide limited insight into safety culture and are not a replacement for a safety culture assessment, but they may assist organizations in identifying improvement opportunities. This paper outlines potential ways that the indicators could be used in practice, resources required, data collection and interpretation strategies. The paper concludes by outlining the limitations of the research and potential future directions.
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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 | 0.216 | 0.392 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".