Pediatric to Adult Health Care Transition Preparation, Support, and Education Unmet Needs of Families Living With a Kawasaki Disease Diagnosis
Bibliographic record
Abstract
Background: Pediatric transition programs aim to maximize autonomy, self-care, and lifelong functioning. Feedback from patients with Kawasaki Disease (KD) is valuable for developing a successful transitional program. This survey aimed to build on previous research and explore gaps in support, education, research, timing, and methods for sharing information. Methods: The Supportive Care Framework, an approach for planning and service delivery, guided the development of a 22-item survey (with areas for free-text responses) regarding KD knowledge, gaps, and transition. The sampling frame included online KD websites for voluntary completion by KD patients and parents. Analysis was descriptive. Results: During the 4-month study period in 2024, a total of 438 surveys from KD patients and parents were analyzed. Key areas of concern included health-related issues, long-term consequences, and psychosocial effects. Prominent interests were learning about physical activity, heart-healthy eating, and navigating adult health care. Most preferred methods of learning included webinars, health passports, or KD family education events. Timing for learning varied from at diagnosis, weekly, monthly, yearly, or before transitioning. Conclusions: The findings provide opportunities for enhancing a seamless KD transition program. The use of online methods was prominent for support and learning. Future directions include establishing a transition roadmap for the KD population, which can also serve as a template in transition planning for other chronic disease populations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".