Walking in the shoes of caregivers of children with obesity: supporting caregivers in paediatric weight management
Bibliographic record
Abstract
To incorporate the perspectives and experiences of family caregivers of children with obesity, the KidFit Health and Wellness Clinic, a paediatric weight management programme, embedded feedback opportunities into various stages of programme development. Caregivers were eligible to participate if their children had completed initial 4-week group-based pilot programming or were currently receiving treatment in 10 or 12 week group-based programming. Data were collected through feedback session discussions, audio-recorded, transcribed verbatim and analysed thematically. In total, 6 caregivers participated in the pilot group feedback session and 32 caregivers participated in the structured group feedback sessions. Caregivers reported that healthy lifestyle strategies first communicated by clinic staff to children during group sessions provided expert validation and reinforcement when discussing similar messages at home. Caregivers reported feeling isolated and blamed for causing their children's obesity and appreciated the supportive forum that group-based programming provided for sharing experiences. Since experiences of blame and isolation can burden caregivers of children with obesity, paediatric weight management programmes might consider including peer support opportunities and discussion forums for ongoing social support in addition to education about lifestyle change.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".