The Physician’s Achilles Heel—Surviving an Adverse Event
Bibliographic record
Abstract
Background: Of hospitalized patients in Canada, 7.5% experience an adverse event (AE). Physicians whose patients experience AES often become second victims of the incident. The present study is the first to evaluate how physicians in Canada cope with aes occurring in their patients. Methods: Survey participants included oncologists, surgeons, and trainees at the Foothills Medical Centre, Calgary, AB. The surveys were administered through REDCap (Research Electronic Data Capture, version 9.0: REDCap Consortium, Vanderbilt University, Nashville, TN, U.S.A.). The Brief COPE (Coping Orientation to Problems Experienced) Inventory, the IES-R (Impact of Event Scale–Revised), the Causal Dimension Scale, and the Institutional Punitive Response scale were used to evaluate coping strategies, prevalence of post-traumatic stress, and institutional culture with respect to AES. Results: Of 51 responses used for the analysis, 30 (58.8%) came from surgeons and 21 (41.2%) came from medical specialists. On the IES-R, 54.9% of respondents scored 24 or higher, which has been correlated with clinically concerning post-traumatic stress. Individuals with a score of 24 or higher were more likely to report self-blame (p = 0.00026) and venting (p = 0.042). Physicians who perceive institutional support to be poor reported significant post-traumatic stress (p = 0.023). On multivariable logistic regression modelling, self-blame was associated with an IES-R score of 24 or higher (p = 0.0031). No significant differences in IES-R scores of 24 or higher were observed between surgeons and non-surgeons (p = 0.15). The implications of AES for physicians, patients, and the health care system are enormous. More than 50% of our respondents showed emotional pathology related to an AE. Higher levels of self-blame, venting, and perception of inadequate institutional support were factors predicting increased post-traumatic stress after a patient AE. Conclusions: Our study identifies a desperate need to establish effective institutional supports to help health care professionals recognize and deal with the emotional toll resulting from AES.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".