Pharmacists’ perspectives on the value of reason for use information
Bibliographic record
Abstract
Background: The indication for a particular medication, or its reason for use (RFU), is important information for prescribers, pharmacists and patients but is not often communicated in writing from prescribers to pharmacists. Adding RFU to a prescription and a medication label would ensure that pharmacists are confident that they are providing high-quality, accurate patient care. This study aims to describe the perspectives of pharmacists on how receiving RFU from prescribers would affect their practice and how pharmacists putting this information on prescriptions would affect patients. Methods: Semi-structured qualitative interviews were conducted with 20 pharmacists in Southwestern Ontario. Thematic analysis was used to analyze the interview transcripts, leading to 4 major themes. Results: Pharmacists expressed that RFU should be formatted to ensure that it is of clinical utility via the use of written text and noted that either medical or lay (also known as plain) language would be appropriate for use. Pharmacists indicated that patient privacy should be considered when writing RFU on labels and that patient preference with respect to the addition of RFU should dictate its inclusion on a medication label. Pharmacist access to RFU was universally acknowledged to improve patient safety by providing pharmacists with more information to determine whether the given medication was indicated. Conclusions: This study provides further information about the impact that having access to RFU would have on pharmacy practice and can be used to advocate for the inclusion of RFU information with prescriptions to improve patient outcomes. Can Pharm J (Ott) 2020;153:xx-xx.
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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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".