Weight, Weight Perceptions, and Health and Well-Being Among Canadian Adolescents: Evidence From the 2017-2018 Canadian Community Health Survey
Bibliographic record
Abstract
PURPOSE: The present study examines the extent to which (mis)matched weight and weight perceptions predict adolescents' self-rated health, mental health, and life satisfaction. DESIGN: Quantitative, cross-sectional study. SETTING: Data from the 2017-2018 Canadian Community Health Survey (CCHS)-a nationally representative sample collected by Statistics Canada. PARTICIPANTS: Canadian adolescents aged between 12 and 17 (n = 8,081). MEASURES: The dependent variables are self-rated health, mental health, and life satisfaction. The independent variable is (mis)matched weight and weight perceptions. ANALYSIS: We perform a series of ordinary least squares (OLS) regression models. RESULTS: Overweight adolescents with overweight perceptions are associated with poorer self-rated health (b = -.546, p < .001 for boys; b = -.476, p < .001 for girls), mental health (b = -.278, p < .001 for boys; b = -.433, p < .001 for girls), and life satisfaction (b = -.544, p < .001 for boys; b = -.617, p < .001 for girls) compared to their counterparts with normal weight and normal weight perceptions. Similar patterns have also been observed among normal weight adolescents with overweight perceptions (e.g., normal weight adolescents with overweight perceptions are associated with poorer self-rated health (b = -.541, p < .01 for boys; b = -.447, p < .001 for girls)). CONCLUSION: Normal weight adolescents are not immune to adverse self-rated health, mental health, and life satisfaction because their weight perceptions are also a contributing factor to health and well-being consequences.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".