A Qualitative Exploration of Adherence to Methotrexate in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To identify barriers and facilitators of adherence to methotrexate (MTX) in juvenile idiopathic arthritis (JIA). STUDY DESIGN: We conducted a qualitative cross-sectional study employing semistructured interviews of 20 adolescents with JIA with varying levels of MTX adherence, determined by pharmacy dispense data. We used grounded theory to identify common themes, which we organized into barriers and facilitators, subdivided into World Health Organization adherence domains. RESULTS: The sample had equal representation of adherent and nonadherent patients, and of those prescribed oral and injectable MTX. The most common barriers were patient- or medication-related, including anticipatory nausea, anxiety, busy schedules, side effects, and the color, taste, and odor of MTX. Common facilitators included patient-related (reminders, routines, calming techniques, family, autonomy, time since diagnosis) and medication-related factors (autoinjectors, folic acid), as well as some health care system-related (positive clinic experiences), and socioeconomic factors (community medical knowledge). CONCLUSIONS: Through interviews with adolescents on MTX for JIA and using the World Health Organization's framework, we gained valuable insight on the most salient barriers and facilitators of MTX adherence. These findings can inform interventions to improve medication adherence and optimize patients' quality of life.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| 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".