Researchers’ perspectives on methodological challenges and outcomes selection in interventional studies targeting medication adherence in rheumatic diseases: an OMERACT-adherence study
Bibliographic record
Abstract
BACKGROUND: Research on adherence interventions in rheumatology is limited by methodological issues, particularly heterogeneous outcomes. We aimed to describe researchers' experiences with conducting interventional studies targeting medication adherence in rheumatology and their perspectives on establishing core outcomes. METHODS: Semi-structured interviews using audio conference were conducted with researchers who had conducted an adherence study of any design in the past 10 years. Data collection and thematic analysis were performed iteratively, until saturation. RESULTS: We interviewed 13 researchers, most of whom worked in academia and specialized in epidemiology and/or health services research. We identified three themes: 1) improving measurement of adherence (considering all phases of adherence, using appropriate and relevant measures, and establishing clinically meaningful thresholds); 2) challenges in designing and appraising adherence intervention studies (considering the confusion over a plethora of outcomes, difficulties with powering studies to demonstrate meaningful changes, and suboptimal descriptions of adherence interventions in published studies); and 3) advancing outcome assessment in adherence intervention studies (capturing rationale for developing a core domain set as well as recommendations and anticipated challenges by participants). CONCLUSIONS: Uniquely gathering perspectives from international adherence researchers, our findings led to researcher-informed recommendations for improving adherence research including specifying the targeted adherence phase in designing interventions and studies and providing a glossary of terms to promote consistency in reporting. We also identified recommendations for developing a core domain set for interventional studies targeting medication adherence including involvement of patients, clinicians, and other stakeholders and methodological and practical considerations to establish rigor and support uptake.
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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 | 0.847 | 0.801 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".