Community pharmacist perceptions of drug–drug interactions
Bibliographic record
Abstract
Background: Drug-drug interactions are preventable medication errors that can lead to serious negative outcomes for patients. Community pharmacists are uniquely positioned with their medication knowledge and role in prescription clinical assessment. However, workplace pressures and limitations related to computer systems can lead to drug-drug interactions being missed. There is a lack of information as to how community pharmacists assess drug interactions. Methods: A qualitative study using key informant interviews of community pharmacists was conducted. Pharmacists were questioned on their perceptions and views of drug interactions. Results: Eight community pharmacists participated. Four main themes were identified from the interviews: 1) pharmacist process of identifying drug interactions, 2) tools that help pharmacists assess and respond to drug interactions, 3) challenges in identifying and responding to clinically important drug interactions and 4) measures to avoid missing interactions. Discussion: Community pharmacists experience challenges around their lack of access to patient information, which limits their ability to properly assess drug-drug interactions. In addition, increasing workload pressures have affected their ability to ensure their patients receive optimal pharmaceutical care. There is also a disconnect between the community pharmacy computer systems' alerts and their clinical relevancy to their specific patients. The overall burdens can lead to professional abstinence in the assessment of drug-drug interactions. Conclusion: Community pharmacists are in an ideal position to prevent patients from experiencing drug-drug interactions. However, to further enable them to fulfill this role, increased access to patients' health records, decreased workload and better customization of computer alerts need to occur.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".