Apology after medical errors: a qualitative vignette study:Medical errors: impact of apology and admission on the resolution and compensation of claims
Bibliographic record
Abstract
Studies investigating the impact of apologies and admission of responsibility for medical errors have been primarily observational, making it hard to attach a causal effect to the admission of responsibility and apologies. Second, most research on the settlement of medical malpractice cases were conducted in the US, with its particular litigation laws and culture. In this multi-jurisdictional study, we investigate the impact of apology and admission of responsibility on preferred resolution and compensation of claims. Employing a vignette design, we examine, among a sample of 327 respondents from 10 different countries, whether admission and apology by the doctor impact respondents' preference for resolution through a civil court case, mediation or a disciplinary board, as well as preferred damages for pain and suffering. Admission and apology by the physician in the vignette did not impact respondents' preference for settlement through a civil court case or mediation, nor did it affect the amount respondents found suitable compensation for pains and damages. We perceived the absence of an apology as particularly aggravating. Thematic analysis of open answers reveals that the impact of admission and apology differs for the three resolution modes and is often contextual and conditional. Future (vignette) studies should investigate whether different cases of medical errors yield similar results and whether more knowledgeable or experienced respondents (such as lawyers) would have other preferences and arguments.
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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".