Comparing the Evolution of Risk Culture in Radiation Oncology, Aviation, and Nuclear Power
Bibliographic record
Abstract
OBJECTIVES: All organizations seek to minimize the risks that their operations pose to public safety. This task is especially significant if they deal with complex or hazardous technologies. Five decades of research in quantitative risk analysis have generated a set of risk management frameworks and practices that extend across a range of such domains. Here, we investigate the risk culture in three commercial enterprises that require exceedingly high standards of execution: radiation oncology, aviation, and nuclear power. METHODS: One of the characteristics of high reliability organizations is their willingness to learn from other such organizations. We investigate the extent to which this is true by compiling a database of the major publications on risk within each of the three fields. We conduct a bibliographic coupling analysis on the combined database to identify connections among publications. This analysis reveals the strength of engagement across disciplinary boundaries and the extent of cross-adoption of best practices. RESULTS: Our results show that radiation oncology is more insulated than the other two fields in its adoption and propagation of state-of-the-art risk management tools and frameworks that have transformed aviation and nuclear power into high reliability enterprises with actuarially low risk. CONCLUSIONS: Aviation and nuclear power have established risk cultures that cross-pollinate. In both nature and extent, we found a distinct difference in radiation oncology's engagement with the risk community, and it lags behind the other two fields in implementing best practices that might mitigate or eliminate risks to patient safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".