What patients want to know about their medications. Focus group study of patient and clinician perspectives.
Bibliographic record
Abstract
OBJECTIVE: To describe what patients want to know about their medications and how they currently access information. To describe how physicians and pharmacists respond to patients' information needs. To use patients', physicians', and pharmacists' feedback to develop evidence-based treatment information sheets. DESIGN: Qualitative study using focus groups and a grounded-theory approach. SETTING: Three regions of Canada (British Columbia, Nova Scotia, and Ontario). PARTICIPANTS: Eighty-eight patients, 27 physicians, and 35 pharmacists each took part in one of 19 focus groups. METHOD: Purposeful and convenience sampling was used. A trained facilitator used a semistructured interview guide to conduct the focus groups. Analysis was completed by at least two research-team members. MAIN FINDINGS: Patients wanted both general and specific information when considering medication treatments. They wanted basic information about the medical condition being treated and specific information about side effects, duration of treatment, and range of available treatment options. Physicians and pharmacists questioned the amount of side-effect and safety information patients wanted and thought that too much information might deter patients from taking their medications. Patients, physicians, and pharmacists supported the use of evidence-based treatment information sheets. CONCLUSION: Patients and clinicians each appear to have a different understanding of what and how much information patients should receive about medications. Feedback from patients can be used to develop patient-oriented treatment information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".