Parents' preferences for drug treatments in juvenile idiopathic arthritis: A discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: To examine parents' preferences for drug treatments and health outcomes in juvenile idiopathic arthritis (JIA) and identify demographic and health-related factors that significantly impact choice. METHODS: A discrete choice experiment was conducted with 105 parents of children with JIA who were cared for by a rheumatologist at The Hospital for Sick Children in Canada. Attributes evaluated included "drug treatment," "child reported pain from arthritis," "participation in daily activities," "side effects," "days missed from school," and "cost to you." Multinomial logit regression was used to estimate the relative importance of each attribute level and interaction term. RESULTS: Parents made tradeoffs between characteristics of the drug treatments and health outcomes. "Participation in daily activities" was the most important attribute, followed by "child reported pain from arthritis" and "cost to you." Parents of children with longer disease durations had stronger preferences for improved participation in daily activities, whereas parents of older JIA patients had stronger preferences for improved control of pain. CONCLUSION: Parents of children with JIA demonstrated strong preferences for treatments that reduce pain and improve daily functioning regardless of the associated side effects, level of responsibility required for drug administration, and days missed from school. Parents of children with longer disease durations and those who had been prescribed aggressive therapies had a greater preference for treatment effectiveness. These findings support the need for considering parental preferences in decisions regarding the choice of treatment for JIA.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".