Welcoming Feedback: Using Family Experience to Design a Pediatric Weight Management Program
Bibliographic record
Abstract
OBJECTIVE: To describe an approach using principles of experience-based codesign (EBCD) and quality improvement (QI) to integrate family experience into the development of a pediatric weight management program. METHODS: Clinic development occurred in 3 plan, do, study, act (PDSA) cycles that were driven by family experience data. During these cycles, families were engaged in feedback sessions that informed program development. Staff reflected on feedback and designed and tested changes that would improve service delivery. RESULTS: The first PDSA cycle resulted in the fundamental program parameters and a formalized patient engagement strategy. The second cycle focused on pilot programming, and feedback was used to develop the structured group program. During the third cycle, feedback sessions were embedded into the structured group programs. Program changes included focusing on health rather than weight-based outcomes, adjusting the timing of program offerings, increasing experiential learning opportunities, and providing more opportunities for peer support. CONCLUSIONS: Both EBCD and QI methodologies informed the process of family engagement and program development. This pragmatic approach might be useful for the development of other family-centered pediatric programs.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".