A closer look at the role of apology in error disclosure: a simulation study
Bibliographic record
Abstract
Background: The importance of open communication following harmful medical errors is widely accepted including the role of authentic apology. Yet, disclosure conversations remain difficult for clinicians and offering an authentic apology is challenging. Purpose: To better understand how clinicians can improve disclosures and apologies by using simulation to observe the approach clinicians use in the initial disclosure, where and when apologies occur within these conversations, what content apologies are linked with, who apologizes, and how apologies differ by their timing within the overall disclosure conversation. Methods: Forty-nine simulations of physician-nurse teams from the U.S. and Canada were videotaped planning and disclosing either a medical or surgical error to a patient-actress. Data from the disclosure portions were coded and analyzed using Atlas-Ti to describe the communication approach clinicians use when disclosing errors and the occurrence and timing of apologies within those disclosures. Results: Ninety-eight clinicians participated: 38 MD-RN teams from the U.S. and 11 from Canada. Of the 49 total simulated error disclosures, 30 involved medical teams disclosing an insulin overdose; 19 were surgical teams disclosing a lost specimen. The average length of the error disclosure conversations was 9.8 minutes (range = 6.1-14.2 min) and tended to follow a similar roadmap. On average, teams offered 2-3 apologies per disclosure (range = 0-9). These apologies occurred at all points during the disclosures and were offered by both physician and nurse participants. Discussion: Clinicians approached the initial disclosure conversations by addressing nine topics in a relatively consistent order. Apologies occurred throughout the disclosures. With opening comments, clinicians apologized to foreshadow bad news; with closing comments, they linked their remorse to broader professional and organizational goals around patient safety and transparency. Within the disclosure, clinicians sometimes linked the apology to their own emotional experience. More frequently, they linked apologies to the patient's emotional response, which may be more effective to ensure that patients hear that the clinicians' remorse is linked to patient suffering rather than clinician discomfort. To improve these difficult discussions, training materials and guidelines for communicating with patients after harm should reflect the complex role that apologies play.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".