Learning from near misses: from quick fixes to closing off the Swiss-cheese holes: Table 1
Bibliographic record
Abstract
INTRODUCTION: The extent to which individuals in healthcare use near misses as learning opportunities remains poorly understood. Thus, an exploratory study was conducted to gain insight into the nature of, and contributing factors to, organisational learning from near misses in clinical practice. METHODS: A constructivist grounded theory approach was employed which included semi-structured interviews with 24 participants (16 clinicians and 8 administrators) from a large teaching hospital in Canada. RESULTS: This study revealed three scenarios for the responses to near misses, the most common involved 'doing a quick fix' where clinicians recognised and corrected an error with no further action. The second scenario consisted of reporting near misses but not hearing back from management, which some participants characterised as 'going into a black hole'. The third scenario was 'closing off the Swiss-cheese holes', in which a reported near miss generated corrective action at an organisational level. Explanations for 'doing a quick fix' included the pervasiveness of near misses that cause no harm and fear associated with reporting the near miss. 'Going into a black hole' reflected managers' focus on operational duties and events that harmed patients. 'Closing off the Swiss-cheese holes' occurred when managers perceived substantial potential for harm and preventability. Where learning was perceived to occur, leaders played a pivotal role in encouraging near-miss reporting. CONCLUSION: To optimise learning, organisations will need to determine which near misses are appropriate to be responded to as 'quick fixes' and which ones require further action at the unit and corporate levels.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".