Exploring strategies to support medication adherence in patients with inflammatory arthritis: a patient-oriented qualitative study using an interactive focus group activity
Bibliographic record
Abstract
Objective: Medication non-adherence is a substantial problem among patients with inflammatory arthritis (IA). Our aim was to explore IA patients’ perspectives on strategies to support medication adherence. Methods: We collaborated with a leading arthritis patient group and conducted a qualitative study on individuals with IA who were taking at least one medication for their IA. An experienced facilitator led participants through a focus group exercise where participants were asked to design, and then discuss, strategies and/or tools supporting medication use. We applied thematic analysis using an iterative, constant comparative approach. Results: We studied six focus groups with 27 participants diagnosed with rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis and comparatively under-represented conditions in this research area such as Sjögren’s syndrome. Five themes emerged throughout the analysis. Two themes – 1) adapting to life with IA and 2) the complexities and dynamic nature of taking medications – describe learning to live with a chronic condition and the challenges encountered when using long-term medications. Three themes – 3) developing lifestyle strategies for medication use (eg, having physical reminders and prompts), 4) becoming informed about medications (eg, information at time of diagnosis, means of receiving information) and 5) receiving support (eg, from health care team members, from family) – offer perspectives on facilitators to medication use. From the relationship between the latter themes, a framework was developed that encompasses means of receiving information and support as actionable targets for patient-oriented adherence interventions for IA. Conclusion: This patient-oriented study highlights the importance of developing timely adherence interventions for IA. Our findings also led to a framework describing means of receiving information, such as through digital media and support, including from health care team members and family, as actionable targets for patient-oriented adherence interventions for IA. Keywords: inflammatory arthritis, medication adherence, concordance, facilitators, barriers, qualitative research, synthetic DMARDs, biologic DMARDs
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".