Hierarchy of controls in Contra Costa Health Services (<scp>CCHS</scp>) incident investigations
Bibliographic record
Abstract
Abstract Industrial safety ordinance (ISO), implemented in Contra Costa County in 1998, and in the city of Richmond in 2000, expands on the California accidental release program (CalARP) and requires the consideration of inherently safer design (ISD). Identifying ISD‐based corrective actions as part of incident investigations is an opportunity to consider ISD within the existing process safety management framework. The objectives of this work were to analyze corrective actions from ISO incident investigation reports with respect to the hierarchy of controls, to identify additional ISD‐based corrective actions for an incident using the ISD bow tie protocol, and to develop additional ISD example‐based guidance. Thirty incidents from 2001 to 2018 were analyzed, and 227 recommended corrective actions were documented and categorized according to the hierarchy of controls. An incident at the Tesoro Golden Eagle Refinery on September 15, 2004 was analyzed, and five additional ISD‐based corrective actions were identified. This work suggests that using the ISD bow tie protocol and example‐based guidance, in addition to ISD checklists and guidewords in incident investigations could help identify more ISD‐based corrective actions.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".