Emotional Impact of Patient Safety Incidents on Family Physicians and Their Office Staff
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate the emotional responses and coping strategies that family physicians and their office staff reported in response to a patient safety incident. METHOD: Two questions contained in a patient safety incident report developed for a study of patient safety in family practice were analyzed. The questions asked reporters to indicate their emotional response to a patient safety incident and how they coped with it. A total of 264 confidential patient safety incident reports collected from September 2007 to August 2010 were analyzed. RESULTS: An emotional response was reported on 82.4% of reports. Of those reports on which an emotional response was reported, a coping strategy was reported on 62.8%. The top 4 reported emotional responses were frustration (48.3%), embarrassment (31.5%), anger (12.6%), and guilt (10.1%). Physicians reported an emotional response more often than clinic staff. An emotional response was reported more often when there was a possibility of harm. Coping strategies were reported as follows: 52% talked to someone about the incident, 37.2% did nothing in response to the incident, 17.9% told the patient about the incident, and 3.6% did something else. Female physicians reported using coping strategies less often than male physicians. A coping strategy was reported more often when there was a possibility of harm. CONCLUSIONS: All members of the health care team report experiencing emotions related to patient safety incidents in their practice. Incidents with minor or no harm still invoked emotional responses from the providers. It is important to understand the impact that patient safety incidents have on the medical clinic as a whole.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".