Patient Perspectives on Outcome Domains of Medication Adherence Trials in Inflammatory Arthritis: An International OMERACT Focus Group Study
Bibliographic record
Abstract
OBJECTIVE: To describe the perspectives of patients with inflammatory arthritis (IA) on outcome domains of trials evaluating medication adherence interventions. METHODS: Adult patients (≥ 18 yrs) with IA taking disease-modifying antirheumatic drugs from centers across Australia, Canada, and the Netherlands participated in 6 focus groups to discuss outcome domains that they consider important when participating in medication adherence trials. We analyzed the transcripts using inductive thematic analysis. RESULTS: Of the 38 participants, 23 (61%) had rheumatoid arthritis and 21 (55%) were female. The mean age was 57.3 ± (SD 15.0) years. Improved outcome domains that patients wanted from participating in an adherence trial were categorized into 5 types: medication adherence, adherence-related factors (supporting adherence; e.g., medication knowledge), pathophysiology (e.g., physical functioning), life impact (e.g., ability to work), and economic impact (e.g., productivity loss). Three overarching themes reflecting why these outcome domains matter to patients were identified: how taking medications could improve patients' emotional and physical fitness to maintain their social function; how improving knowledge and confidence in self-management increases patients' trust and motivation to take medications as agreed with minimal risk of harms; and how respect and reassurance, reflecting health care that values patients' opinions and is sensitive to patients' individual goals, could improve medication-taking behavior. CONCLUSION: Patients value various outcome domains related to their overall well-being, confidence in medication use, and patient-healthcare provider relationships to be evaluated in future adherence trials.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".