“Girls don’t have big tummies”: The experiences of weight-related discussions for children with autism spectrum disorders
Bibliographic record
Abstract
Children with autism spectrum disorders appear to be at a higher risk of having obesity than their typically developing peers. Although it has been recommended that healthcare providers speak to children with autism spectrum disorders about the potential health risks of unhealthy weight, no previous research has explored how healthcare providers communicate with them about this topic. The purpose of this study was to explore children's perspectives and experiences of discussing weight-related topics in healthcare consultations. Eight children were interviewed, and an interpretive phenomenological analysis informed the research approach and analysis of the data. Results indicated that weight-related discussions with healthcare providers were often met with trepidation, anxiety, anger, and frustration. Children also expressed that they experienced weight stigma in clinical visits and everyday interactions. Weight stigma was often (unwittingly) projected by healthcare providers during appointments and had debilitating effects on children. Finally, higher weights emerged as a repetitive/restricted interest, and children reported body image challenges regarding their higher weights. Frameworks and tools that are specific to the needs and abilities of children with autism spectrum disorders are needed for healthcare providers to foster positive conversations about weight-related topics in an effort to promote lifelong wellness.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".