Patient-Related Risk Factors for Self-Reported Medication Errors in Hospital and Community Settings in 8 Countries
Bibliographic record
Abstract
BACKGROUND: Medication errors can cause substantial harm to patients and may lead to significant costs within a health care system. As such, there is value in identifying patient-related risk factors for medication errors. The objectives of this study were to identify patient-related risk factors associated with self-reported medication errors and to determine whether the risk factors differed between hospital and community settings. METHODS: The Commonwealth Fund's 2008 International Health Policy Survey of chronically ill patients in 8 countries was the primary data source. Univariate analyses were used to determine significant explanatory variables (p < 0.05) for inclusion in weighted logistic regression models. Two regression models were developed: one to identify overall patient-related risk factors and the other to determine whether these factors differed between hospital and community settings. RESULTS: The final study population consisted of 9944 adults. Patient-related risk factors significantly associated with self-reported medication errors were the number of medications being taken, sex, age and country of residence. Approximately 4 out of every 5 self-reported medication errors occurred in the community setting. CONCLUSIONS: A substantial percentage of patients with chronic diseases in the countries covered by the survey experienced medication errors, with most errors occurring in the community setting. Several patient-related risk factors were associated with these errors. Greater emphasis on national incident reporting systems and greater sharing of knowledge across nations could help to identify strategies to overcome these problems. More specifically, strategies to increase reporting of and learning from medication errors, as well as education about potential patient-related risk factors, are recommended.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".