Choosing Your Words Carefully
Bibliographic record
Abstract
BACKGROUND: A gap exists between patients' desire to be told about medical errors and present practice. Little is known about how physicians approach disclosure. The objective of the present study was to describe how physicians disclose errors to patients. METHODS: Mailed survey of 2637 medical and surgical physicians in the United States (Missouri and Washington) and Canada (national sample). Participants received 1 of 4 scenarios depicting serious errors that varied by specialty (medical and surgical scenarios) and by how obvious the error would be to the patient if not disclosed (more apparent vs less apparent). Five questions measured what respondents would disclose using scripted statements. RESULTS: Wide variation existed regarding what information respondents would disclose. Of the respondents, 56% chose statements that mentioned the adverse event but not the error, while 42% would explicitly state that an error occurred. Some physicians disclosed little information: 19% would not volunteer any information about the error's cause, and 63% would not provide specific information about preventing future errors. Disclosure was affected by the nature of the error and physician specialty. Of the respondents, 51% who received the more apparent errors explicitly mentioned the error, compared with 32% who received the less apparent errors (P<.001); 58% of medical specialists explicitly mentioned the error, compared with 19% of surgical specialists (P<.001). Respondents disclosed more information if they had positive disclosure attitudes, felt responsible for the error, had prior positive disclosure experiences, and were Canadian. CONCLUSIONS: Physicians vary widely in how they would disclose errors to patients. Disclosure standards and training are necessary to meet public expectations and promote professional responsibility following errors.
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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.288 | 0.339 |
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".