Empowering Young People Living With Juvenile Idiopathic Arthritis to Better Communicate With Families and Care Teams: Content Analysis of Semistructured Interviews
Bibliographic record
Abstract
BACKGROUND: Young people living with juvenile idiopathic arthritis (JIA) face a number of communication barriers for achieving optimal health as they transition from pediatric care into adult care. Despite growing interest in mobile or wireless technologies to support health (mHealth), it is uncertain how these engagement tools might support young people, their families, and care teams to optimize preference-based treatment strategies. OBJECTIVE: This study aims to examine how an mHealth patient support system (mPSS) might foster partnership between young people living with JIA, their families, and care teams. METHODS: Semistructured interviews with young people (5-15 years old), their families, and JIA care teams were conducted using researcher-developed interviews guides. Transcribed data were qualitatively analyzed using conventional content analysis. RESULTS: We conducted semistructured interviews with 15 young people, their parents, and 4 care team members. Content analysis revealed the potential of an mPSS to support productive dialogue between families and care teams. We identified four main themes: (1) young people with JIA face communication challenges, (2) normalizing illness through shared experience may improve adherence, (3) partnership opens windows into illness experiences, and (4) readiness to engage appears critical for clinic implementation. CONCLUSIONS: A human-centered mPSS design that offers JIA patients the ability to track personally relevant illness concerns and needs can enhance communication, generate consensus-based treatment decisions, and improve efficiency and personalization of care. Technology that supports continuous learning and promotes better understanding of disease management may reduce practice burden while increasing patient engagement and autonomy in fostering lasting treatment decisions and ultimately supporting personalized care and improving 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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".