Weight Perception and Weight-control Intention among Youth in the COMPASS Study
Bibliographic record
Abstract
OBJECTIVES: Youth weight perceptions and the association with weight-control intentions were explored by sex and body mass index (BMI). METHODS: Cross-sectional analyses (frequency, chi-square, multiple logistic regression) were conducted among 44,861 grade 9-12 students in Year 2(Y2:2013-2014) of the COMPASS study, adjusting for sex and race/ethnicity. RESULTS: Overall, weight underestimations were more common than overestimations, although there were differences by sex and race/ethnicity. Boys were relatively more likely to underestimate their weight status, and girls were more likely to overestimate. Compared to youth with normal-weight BMIs, students with overweight BMIs were more inclined to underestimate their weight, and those with BMIs in the obese range had higher odds of accurately perceiving their weight. Regardless of BMI, youth with overweight perceptions were more likely to report trying to lose weight than those who perceived their weight to be "about right," whereas youth with underweight perceptions tended to report efforts to gain weight. CONCLUSIONS: While youth with overweight BMIs did not necessarily perceive their weight as such, accurate perceptions were more likely once BMI reached the obese range. Results suggest weight perception is a more useful predictor of weight-control intentions than self-reported weight status among youth.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".