“Fat is really a four‐letter word”: Exploring weight‐related communication best practices in children with and without disabilities and their caregivers
Bibliographic record
Abstract
BACKGROUND: Health care professionals play a critical role in preventing and managing childhood obesity, but the American Academy of Pediatrics recently stressed the importance of using sensitive and nonstigmatizing language when discussing weight with children and families. Although barriers to weight-related discussions are well known, there are few evidence-based recommendations around communication best practices. Disability populations in particular have previously been excluded from work in this area. The objectives were to present the findings of a recent scoping review to children with and without disabilities and their caregivers for their reactions; and to explore the experiences and perceptions of the children and their caregivers regarding weight-related communication best practices. METHODS: Focus group and individual interviews were conducted with 7-18-year olds with and without disabilities and their caregivers. The interview guide was created using findings from a recent scoping review of weight-related communication best practices. Inductive thematic analysis was employed. RESULTS: Eighteen children (9 boys; 7 children with disabilities) and 21 caregivers (17 mothers, 1 step-father, 3 other caregivers) participated in 8 focus group and 7 individual interviews. Preferred communication strategies were similar across those with and without disabilities, although caregivers of children with autism spectrum disorder endorsed more concrete approaches. Discussions emphasizing growth and health were preferred over weight and size. Strengths-based, solution-focused approaches for weight conversations were endorsed, although had not been widely experienced. CONCLUSION: Perceptions of weight-related communication were similar across stakeholder groups, regardless of children's disability or weight status. Participants generally agreed with the scoping review recommendations, suggesting that they apply broadly across different settings and populations; however, tailoring them to specific circumstances is critical. Empirical evaluations are still required to examine the influence of weight-related communication on clinically important outcomes, including behaviour change and family engagement in care.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".