Facilitators and Barriers to Adherence in the Initiation Phase of Disease-modifying Antirheumatic Drug (DMARD) Use in Patients with Arthritis Who Recently Started Their First DMARD Treatment
Bibliographic record
Abstract
OBJECTIVE: To explore themes associated with adherence in the initiation phase for first-time use of disease-modifying antirheumatic drugs (DMARD) in patients with inflammatory arthritis using focus groups and individual interviews. METHODS: Thirty-three patients were interviewed in focus groups and individual interviews. Interviews were transcribed verbatim and imported into ATLAS.ti software (Scientific Software Development GmbH). Responses that included reasons for adherence or nonadherence in the initiation phase were extracted and coded by 2 coders separately. The 2 coders conferred until consensus on the codes was achieved. Codes were classified into overarching themes. RESULTS: Five themes emerged: (1) symptom severity, (2) experiences with medication, (3) perceptions about medication and the illness, (4) information about medication, and (5) communication style and trust in the rheumatologist. CONCLUSION: Perceptions about medication and the communication style with, and trust in, the rheumatologist were mentioned the most in relation to starting DMARD. The rheumatologist plays a crucial role in influencing adherence behavior by addressing perceptions about medication, providing information, and establishing trust in the treatment plan.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".