A Call to Action: Setting the Research Agenda for Addressing Obesity and Weight-Related Topics in Children with Physical Disabilities
Bibliographic record
Abstract
BACKGROUND: Pediatric obesity is a world-wide challenge. Children with physical disabilities are particularly at risk of obesity, which is worrisome because obesity can result in serious secondary conditions that decrease health status, reduce independence, and increase impact on healthcare systems. However, the determinants of obesity and the health promotion needs of children with physical disabilities are relatively unexplored compared with their typically developing peers. METHODS: This white paper describes a Canadian multi-stakeholder workshop on the topic of obesity and health in children with physical disabilities and provides recommendations for future research in this understudied area. RESULTS: Seventy-one knowledge gaps identified by attendees using a modified nominal group technique clustered into six themes: (1) early, sustained engagement of families; (2) rethinking determinants of obesity and health; (3) maximizing impact of research; (4) inclusive integrated interventions; (5) evidence-informed measurement and outcomes; and (6) reducing weight biases. Attendees worked together to develop research plans in more detail for three areas identified through consensus as high priority: "early, sustained engagement of families;" "rethinking determinants of obesity and health;" and "evidence informed measurement and outcomes." CONCLUSIONS: Using the workshop described here as a call to action, Canadian researchers are now well positioned to work toward a greater understanding of weight-related topics in children with physical disabilities, with the aim of developing evidence-based and salient obesity prevention and treatment approaches.
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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 | 0.098 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.024 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.020 | 0.031 |
| 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".