A balanced approach to using organizational patient safety incident data for research
Bibliographic record
Abstract
Reported patient safety incidents offer high-value perspectives on safety threats but can be an untapped source of learning due to their sensitive nature and the presence of potential data protected under Quality Assurance (QA) legislations. There are no published guidelines for leaders to enable ethical use of data protected under QA legislation in reported patient safety incidents within the Canadian context. Liberating this data requires understanding the appropriate purposes for use, which draws on ethical and privacy-related considerations. We describe the approach followed to balance the duty to protect relevant privacy interests with the moral obligation to conduct research, and the proactive prevention of patient harm at our Canadian multi-site academic health sciences centre. Overall, we developed guidelines and discovered leaders must commit to establishing connections between organizational governance, legal structures, and privacy experts to support research enabling learning from patient safety incidents.
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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 | 0.651 | 0.483 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.025 | 0.047 |
| Scholarly communication | 0.045 | 0.034 |
| Open science | 0.011 | 0.047 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".