Catching and correcting near misses: The collective vigilance and individual accountability trade-off
Bibliographic record
Abstract
Despite the focus on patient safety and quality health care for the last two decades, there is still limited understanding of how interprofessional interactions at an organizational or work unit level influence how clinicians perceive and respond to safety events and errors. Within the rubric of safety events, there has been a growing interest in near misses as precursors to adverse events in health care. Given the interactive nature of the variety of professionals working together in the delivery of health care, understanding how the different clinicians experience and respond to near misses in practice is important. A constructivist grounded theory approach was employed for this study which included semi-structured interviews with 24 participants in a large teaching hospital in Canada. Findings from this study provide a deeper understanding into how different clinicians experience and respond to near misses in clinical practice. This understanding indicates that collective vigilance can potentially create risk by eroding individual professional accountability through reliance on other team members to catch and correct their errors. Further research is needed to explore in more depth the trade-offs between collective vigilance and individual accountability by relying on others to catch and correct the potentially harmful errors and avert negative outcomes.
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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 | 0.024 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".