Patient-Related Risk Factors for the Occurrence of Patient-Reported Medication Errors in One Community Pharmacy
Bibliographic record
Abstract
Background: Medication errors have been shown to occur 4 times more often in the community compared to the hospital setting. Therefore, identifying the patient-related factors within the community that contribute to an increased occurrence of medication errors is required. Objective: To assess patients’ knowledge and understanding of their medications in order to determine risk for medication errors. Methods: This quantitative descriptive study used a convenience sample of participants filling their prescriptions at one independent pharmacy in Canada. The study used descriptive statistics including frequencies and correlations. Further multiple regressions were conducted to explore the relationship between the patient factors and medication knowledge and use. Results: A total of 33.5% of respondents indicated that they know what medication they are currently taking, and that they know why they taking their current medications. Decreased knowledge of medication taken was significantly associated with likelihood of a medication error by 3.6 times ( P = .048). Increased age ( P = .01) and the death of a spouse ( P = .01) correspond to a decreased knowledge of medications. Those with less education appeared to have decreased understanding as to why they are taking their medications ( P = .01). The odds of experiencing a medication error increased with multiple medications. Also, changes in medication dose increased the probability of experiencing a medication error by 2.2 times. This study however had a small sample size. Conclusions: With identification and understanding of patient factors that influence the incidence of medication error, we can increase awareness and determine solutions to decrease risk of medication error in clinical practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".