Determinants of patient-reported medication errors: a comparison among seven countries
Bibliographic record
Abstract
OBJECTIVE: Medication errors are a frequent cause of adverse drug events and a major concern for patient safety. This study compared the predictors of error among seven countries (Australia, Canada, New Zealand, the United Kingdom, the United States, Germany and the Netherlands). METHODS: We conducted a cross-sectional study using the 2007 Commonwealth Fund International Health Policy Survey data. The outcome was patient-reported error in the past 2 years. Possible predictors were studied using logistic regression. RESULTS: Eleven thousand nine hundred and ten respondents were included in this analysis, of which 1291 respondents (11%) had experienced error. Poor coordination of care was a shared concern of all seven countries [adjusted odds ratios (ORs) ranged from 2.1 (95% CI: 1.3-3.5) to 3.0 (95% CI: 2.1-4.5)]. Cost-related barriers to medical services/medicines was also a predictor in six countries [ORs ranged from 1.9 (95% CI: 1.5-2.6) to 2.6 (95% CI: 1.5-4.6)]. Other common risk factors across countries included seeing multiple specialists, multiple chronic conditions, hospitalisation and multiple emergency room visits. Cross-country heterogeneity in contributing factors included age and specific chronic condition. Number of medications, number of doctor visits, household income and education level were not associated with error in most countries. CONCLUSION: Poor coordination of care is a key risk factor in all seven countries. Cost-related barriers were also associated with an increased likelihood of error. The major challenge for all countries for error prevention is better communication among multiple healthcare providers and more structured organisation of care across healthcare settings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".