Prescribers’ perspectives on including reason for use information on prescriptions and medication labels: a qualitative thematic analysis
Bibliographic record
Abstract
BACKGROUND: The indication for prescribing a particular medication, or its reason for use (RFU) is a crucial piece of information for all those involved in the circle of care. Research has shown that sharing RFU information with physicians, pharmacists and patients improves patient safety and patient adherence, however RFU is rarely added on prescriptions by prescribers or on medication labels for patients to reference. METHODS: Qualitative interviews were conducted with 20 prescribers in Southern Ontario, Canada, to learn prescribers' current attitudes on the addition of RFU on prescriptions and medication labels. A trained interviewer used a semi-structured interview guide for each interview. The interviews explored how the sharing of RFU information would impact prescribers' workflows and practices. Interviews were recorded, transcribed and thematically coded. RESULTS: The analysis yielded four main themes: Current Practice, Future Practice, Changing Culture, and Collaboration. Most of the prescribers interviewed do not currently add RFU to prescriptions. Prescribers were open to sharing RFU with colleagues via a regional database but wanted the ability to provide context for the prescribed medication within the system. Many prescribers were wary of the impact of adding RFU on their workflow but felt it could save time by avoiding clarifying questions from pharmacists. Increased interprofessional collaboration, increased patient understanding of prescribed medications, avoiding guesswork when determining indications and decreased misinterpretation regarding RFU were cited by most prescribers as benefits to including RFU information. CONCLUSIONS: Prescribers were generally open to sharing RFU and clearly identified the benefits to pharmacists and patients if added. Critically, they also identified benefits to their own practices. These results can be used to guide the implementation of future initiatives to promote the sharing of RFU in healthcare teams.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".