Perceptions of preventable medical errors in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: (i) To compare public perceptions of the frequency, responsibility, causes and solutions for preventable medical errors for persons who report and do not report having experienced a preventable medical error while receiving healthcare services in Alberta, Canada. (ii) To describe public opinion about confidentiality and disclosure of preventable medical error. (iii) To examine the relationship between reporting preventable medical error and perceived quality of the healthcare system. METHODS: Population-based telephone survey. Households selected by random digit dialing and individual in household selected by most recent birthday. Province of Alberta, Canada. Representative sample of adult Albertans (N = 1500). Public perceptions of the frequency, responsibility, causes and solutions for preventable medical error; opinions about confidentiality and disclosure; perceived quality of the healthcare system. RESULTS: Five hundred and fifty-nine (37.3%; 95% CI 34.8-39.8%) of 1500 respondents reported that they or a family member had ever experienced a preventable medical error while receiving health care in Alberta, Canada. Respondents who reported a preventable medical error were more likely to believe that preventable medical errors occur with greater frequency, were less likely to think that their doctor would tell them if a preventable medical error was made in their care, and tended to rate the quality of the healthcare system less favourably. CONCLUSION: This paper provides healthcare managers and policymakers with insight into the public's perceptions of preventable medical error and may facilitate the development of strategies to improve patient safety, public confidence and public satisfaction with the healthcare system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".