Multisite Randomized Clinical Trial Evaluating an Online Self-Management Program for Adolescents With Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy in improving pain and health-related quality of life (HRQOL) of an online self-management program for adolescents with juvenile idiopathic arthritis (JIA). METHODS: Youth ages 12-18 years with JIA were recruited from 10 rheumatology clinics across the United States and randomized to complete an online self-management program (n = 144) or an online disease education program (n = 145). Participants in the self-management group worked through multimedia-based modules comprising psychoeducation, training in cognitive-behavioral coping skills and stress management, and other self-management topics over a 12-week period. Participants in the control group viewed a series of preselected quality educational websites about JIA over the same interval. Online content for both groups was made available in English and Spanish to facilitate inclusion of Hispanic participants. Blinded assessment of main outcomes (pain intensity, pain interference, and HRQOL) and process outcomes (disease knowledge, self-efficacy, pain coping, and emotional adjustment) occurred at baseline, posttreatment, and at 6- and 12-month postrandomization follow-up visits. RESULTS: Participants on average demonstrated significant improvements over the study period in the main outcomes, with no significant group differences in the degree of improvement. Effect sizes for these improvements were small. The amount of improvement in self-efficacy, emotional avoidance coping, disease knowledge, and emotional functioning in part predicted improvement in pain and HRQOL outcomes. CONCLUSIONS: Primarily self-directed online self-management training and online disease education comparably and modestly improve pain and HRQOL in youth with 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".