Sex disparity and perception of obesity/overweight by parents and grandparents
Bibliographic record
Abstract
OBJECTIVE: To explore the factors associated with the sex disparity showing a greater prevalence of obesity/overweight in boys compared with girls in Chinese school children. METHODS: Sampled students and their parents were asked to complete a questionnaire. Perceptions of weight status by the parents, grandparents and children themselves were collected. A logistic regression analysis was used. RESULTS: The sampled students included 327 obese/overweight students and 1078 students with normal body mass index (BMI). The crude OR of obesity/overweight for boys compared with girls was 1.57 (95% CI 1.22 to 2.01). The increased risk of childhood obesity/overweight for boys remained after adjustment for prenatal and infant factors, daily habits and family situation, but disappeared after adjustment for perception of weight status (OR 1.27 [95% CI 0.93 to 1.67]). There were differences in underestimation of children's weight status between boys and girls by their parents and grandparents (OR 1.33 [95% CI 1.08 to 1.64] and OR 1.42 [95% CI 1.15 to 1.75], respectively). CONCLUSIONS: Misconceptions about a child's weight status were prevalent among parents and grandparents, and boys' weight status was more frequently underestimated than girls. The disparity of underestimating weight according to sex may partially contribute to the difference in the prevalence of obesity/overweight between boys and girls among Chinese school children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".