Development of the MapMe intervention body image scales of known weight status for 4–5 and 10–11 year old children
Bibliographic record
Abstract
Background: Parents tend to visually assess children to determine their weight status and typically underestimate child body size. A visual tool may aid parents to more accurately assess child weight status and so support strategies to reduce childhood overweight. Body image scales (BIS) are visual images of people ranging from underweight to overweight but none exist for children based on UK criteria. Our aim was to develop sex- and age-specific BIS for children, based on British growth reference (UK90) criteria. Methods: BIS were developed using 3D surface body scans of children, their associated weight status using UK90 criteria from height and weight measurements, and qualitative work with parents and health professionals. Results: Height, weight and 3D body scans were collected (211: 4-5 years; 177: 10-11 years). Overall, 12 qualitative sessions were held with 37 participants. Four BIS (4-5-year-old girls and boys, 10-11-year-old girls and boys) were developed. Conclusions: This study has created the first sex- and age-specific BIS, based on UK90 criteria. The BIS have potential for use in child overweight prevention and management strategies, and in future research. This study also provides a protocol for the development of further BIS appropriate to other age groups and ethnicities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".