Association of Medication Beliefs, Self-efficacy, and Adherence in a Diverse Cohort of Adults with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) patients' adherence to disease-modifying antirheumatic drugs (DMARD) is often suboptimal. We examined associations among medication beliefs, self-efficacy, and adherence to medications in RA. METHODS: Data were from a longitudinal observational cohort of persons with RA. Subjects completed telephone interviews on self-reported adherence, self-efficacy, demographics, and the Beliefs about Medicines Questionnaire (BMQ), which assesses beliefs in necessity and beliefs about taking medication. Bivariate and multivariate logistic regression identified correlates of poor adherence to synthetic DMARD and prednisone as well as to biologic therapy, including medication concerns and necessity. RESULTS: There were 362 patients who reported taking a synthetic DMARD and/or prednisone. Of these, 14% and 21% reported poor adherence to oral DMARD or prednisone, and biologics, respectively. There were 64% who reported concern about taking medicines, 81% about longterm effects, and 47% about becoming too dependent on medicines. In multivariate analyses, the BMQ necessity score was independently associated with better adherence to oral DMARD or prednisone (adjusted OR 0.61, 95% CI 0.41-0.91), while self-efficacy was associated with greater odds of poor adherence to oral medications (adjusted OR 1.23, 95% CI 1.01-1.59). Beliefs in medicines and self-efficacy were not associated with adherence to biologics. CONCLUSION: In a diverse cohort of patients with RA, stronger beliefs in the necessity of medication were associated with better adherence to oral DMARD or prednisone, while higher self-efficacy was associated with poor adherence. Providers can play important roles in eliciting patient beliefs about medications to improve adherence and ultimately health outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".