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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".