Body size dissatisfaction among young Chinese children in Hong Kong: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To determine the potential predictors of body size dissatisfaction in Chinese children. DESIGN: The Child's Body Image Scale was used to assess body size perception and dissatisfaction. BMI was calculated from objectively measured height and weight. Predictors of body size dissatisfaction were examined by logistic regression analysis. SETTING: Hong Kong, China. SUBJECTS: Six hundred and twenty children (53 % boys, aged 6·1-12·9 years) from a state-run primary school. RESULTS: Female sex (adjusted OR (AOR)=1·91; 95 % CI 1·32, 2·76), age (AOR=2·62; 95 % CI 1·65, 4·16 for 8-10 years; AOR=2·16; 95 % CI 1·38, 3·38 for >10 years), overweight (AOR=6·23; 95 % CI 3·66, 10·60) and obesity (AOR=19·04; 95 % CI 5·64, 64·32) were positively associated with desire to be thinner. Size misperception was a strong predictor of body size dissatisfaction, irrespective of actual weight status (AOR=1·90; 95 % CI 1·02, 3·54 for overestimation; AOR=0·43; 95 % CI 0·27, 0·67 for underestimation). CONCLUSIONS: Body size dissatisfaction is prevalent among Chinese children as young as 6 years. Female sex, age, overweight, obesity and overestimation of size were associated with increased desire to be thinner. These findings emphasise the importance of preventing body image issues from an early age.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".