Self-Reported Medical Errors in Seven Countries: Implications for Canada
Bibliographic record
Abstract
The purpose of this study was to determine the rate of self-reported errors in Canada compared with other countries, and to identify risk factors for medical error. In 2007, the Commonwealth Fund surveyed a sample of adults in seven industrialized nations, including Canada. Data from this source were used to perform a bivariate analysis comparing those individuals who reported having experienced a medical error with those who did not, followed by a logistic regression model to delineate the relationship between medical error and several explanatory variables. Overall, 11,910 respondents from seven countries were included in the analysis. The rate of self-reported medical error ranged from 12 to 20% in the seven nations. Approximately one in six Canadians reported having experienced at least one error in the past two years, which translates to 4.2 million adult Canadians. Several variables were found to have a statistically significant relationship to self-reported medical errors in the final regression model, including high prescription drug use, the presence of a chronic condition, a lack of physician time with the patient, age under 65, a lack of patient involvement in care, perceived inadequate nursing staffing and an absence of a regular doctor. Identification of several patient, provider and system characteristics associated with self-reported medical error should aid in the development of strategies to address this problem by healthcare decision-makers and clinicians.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".