Categorizing Errors and Adverse Events for Learning: A Provider Perspective
Bibliographic record
Abstract
There is little agreement in the literature as to what types of patient safety events (PSEs) should be the focus for learning, change and improvement, and we lack clear and universally accepted definitions of error. In particular, the way front-line providers or managers understand and categorize different types of errors, adverse events and near misses and the kinds of events this audience believes to be valuable for learning are not well understood. Focus groups of front-line providers, managers and patient safety officers were used to explore how people in healthcare organizations understand and categorize different types of PSEs in the context of bringing about learning from such events. A typology of PSEs was developed from the focus group data and then mailed, along with a short questionnaire, to focus group participants for member checking and validation. Four themes emerged from our data: (1) incidence study categories are problematic for those working in organizations; (2) preventable events should be the focus for learning; (3) near misses are an important but complex category, differentiated based on harm potential and proximity to patients; (4) staff disagree on whether events causing severe harm or events with harm potential are most valuable for learning. A typology of PSEs based on these themes and checked by focus group participants indicates that staff and their managers divide events into simple categories of minor and major events, which are differentiated based on harm or harm potential. Confusion surrounding patient safety terminology detracts from the abilities of providers to talk about and reflect on a range of PSEs, and from opportunities to enhance learning, reduce event reoccurrence and improve patient safety at the point of care.
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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 | 0.050 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".