Perceptions of Community Pharmacy Staff regarding Strategies to Reduce and to Improve the Reporting of Medication Incidents
Bibliographic record
Abstract
Background: Medication incidents can have serious consequences for the health of patients and the perceived safety of community pharmacy practice. This study was designed to assess the perceptions of pharmacy staff members about various strategies for reducing and improving the reporting of medications incidents, with the ultimate goal of helping pharmacy managers to determine which practices are likely to be widely accepted. Methods: Staff members of community pharmacies were recruited from 13 pharmacies in Nova Scotia. This convenience sample consisted of pharmacists, pharmacy managers and owners, and pharmacy support staff (i.e., technicians, interns and pharmacy students). The questionnaire had 5 sections, including sections on demographic characteristics, organizational culture within the pharmacies, strategies for reducing and for improving the reporting of medication incidents and existing reporting processes and desired changes, along with an open-ended section on reporting of medication incidents in general. The current article reports data from the 20-question section that sought respondents' perceptions, according to a Likert-type scale, of selected practices for reducing medication incidents and for identifying and disclosing any such incidents that do occur. Multivariate analysis of variance (MANOVA) was used to examine overall differences in mean ratings for various strategies by staff group or ownership type. Follow-up univariate analysis and Tukey test were used to examine differences in staff groups and ownership types for each reduction and reporting strategy. Correlation analyses were performed to determine strategies for which individual respondents had similar perceptions. Only pairs of strategies with moderate or strong correlations ( r ≥ 0.60) are discussed. Results: MANOVA indicated significant differences in the perceived effectiveness of various strategies for reducing and for improving the reporting of medication incidents. Respondents indicated that having clinical pharmacists help physicians to select drug therapies would be the most effective strategy to reduce the frequency of medication incidents, and they thought that sharing with colleagues any lessons learned from incidents that did occur and assuring anonymity of reporting would be the most effective ways to increase the reporting of medication incidents. Conclusions: Instituting strategies for reducing and enhancing the reporting of medication incidents that are viewed as effective by pharmacy staff members may help to increase reporting rates and to reduce the number of such incidents occurring at the community pharmacy.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".