Weight Management for Children With Disabilities: Exploring the Perspectives of Health Care Professionals Working in Pediatric Weight Management Clinics in Canada
Bibliographic record
Abstract
Background:Children with disabilities are twice as likely to have overweight/obesity than their typically developing peers. Higher weights in these individuals may compound challenges already experienced with their disability, including mobility and activities of daily living. However, children with disabilities often find it challenging accessing weight management care. It is therefore important to understand the experiences and needs of the health care professionals (HCPs) who work in specialized pediatric weight management clinics about providing weight-related care to children with disabilities. Methods:Employing an interpretive description approach, purposeful sampling was used to recruit 17 HCP participants working in pediatric weight management settings in Canada. Qualitative semistructured interviews were conducted online or via telephone. All interview recordings were transcribed and a reflexive thematic analysis approach was used to develop themes from the data. Results:Four themes were developed: (1) infrequent referrals leads to a lack of experience with children with disabilities; (2) adapting group-based clinics can be challenging; (3) perceived lack of disability-specific knowledge causes moral distress; and (4) disability-specific training and greater interdisciplinary collaboration are desired. Conclusions:This work identifies the urgent need for more evidence-based, specialized, weight-related treatment options for children with disabilities, as well as more support for HCPs working in existing 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".