How do I keep myself safe? Patient perspectives on including reason for use information on prescriptions and medication labels: a qualitative thematic analysis
Bibliographic record
Abstract
Abstract Background Medications are crucial for maintaining patient wellness and improving health in modern medicine, but their use comes with risks. Helping patients to understand why they are taking medications is important for patient-centered care and facilitates patient adherence to prescribed medications. One strategy involves enhancing communication between patients, physicians, and pharmacists through the sharing of reason for use (RFU) information or the indication for medications. Methods Semi-structured interviews were conducted with 20 patients in Ontario, Canada, to gain perspectives on how patients currently store their medication information and benefits and disadvantages of adding RFU to prescriptions and medication labels. An interview guide was used by the two interviewers, and the interviews were recorded, transcribed, and thematically coded. Results The analysis yielded three main themes: patient decision making with RFU, RFU in modern, patient-centered care, and logistical aspects of communicating RFU. The patients that were interviewed expressed the value of having RFU when deciding if a medication was effective or to stop taking the medication. Patients felt comfortable with RFU being added to prescriptions and acknowledged the value in adding RFU to medication labels, helping patients and others identify and distinguish medications. Patients generally expressed interest in having RFU written in lay language and identified strengths and weaknesses of having access to RFU via a website or app. Conclusions Patients rated the importance of knowing RFU very highly, identified the value in sharing RFU with pharmacists on prescriptions, and in having RFU on medication labels. These results can be used to inform policy on the addition of RFU on prescriptions and medication labels and support improved communication between patients, pharmacists, and physicians about RFU.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".