Patient and Caregiver Priorities for Medication Adherence in Gout, Osteoporosis, and Rheumatoid Arthritis: Nominal Group Technique
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify and prioritize factors important to patients and caregivers with regard to medication adherence in gout, osteoporosis (OP), and rheumatoid arthritis (RA) and to describe the reasons for their decisions. METHODS: Patients with gout, OP, and RA and their caregivers, purposively sampled from 5 rheumatology clinics in Australia, identified and ranked factors that they considered important for medication adherence using nominal group technique and discussed their decisions. An importance score (IS; scale 0-1) was calculated, and qualitative data were analyzed thematically. RESULTS: From 14 focus groups, 82 participants (67 patients and 15 caregivers) identified 49 factors. The top 5 factors based on the ranking of all participants were trust in doctor (IS 0.46), medication effectiveness (IS 0.31), doctor's knowledge (IS 0.25), side effects (IS 0.23), and medication-taking routine (IS 0.13). The order of the ranking varied by participant groupings, with patients ranking "trust in doctor" the highest, while caregivers ranked "side effects" the highest. The 5 themes reflecting the reasons for factors influencing adherence were as follows: motivation and certainty in supportive individualized care; living well and restoring function; fear of toxicity and cumulative harm; seeking control and involvement; and unnecessarily difficult and inaccessible. CONCLUSION: Factors related to the doctor, medication properties, and patients' medication knowledge and routine were important for adherence. Strengthening doctor-patient trust and partnership, managing side effects, and empowering patients with knowledge and skills for taking medication could enhance medication adherence in patients with rheumatic conditions.
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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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".