Are weight status and weight perception associated with academic performance among youth?
Bibliographic record
Abstract
BACKGROUND: Emerging evidence suggests perceptions of being overweight account for many of the psychosocial consequences commonly associated with obesity. Previous research suggests an obesity achievement gap, yet limited research has explored weight perception in association with academic performance. Moreover, underweight perceptions have typically been excluded from research. The current study examined how BMI classification and weight perception relate to academic performance in a large cohort of youth. METHODS: We used cross-sectional survey data from 61,866 grade 9-12 students attending the 122 Canadian schools that participated in Year 6 (2017/2018) of the COMPASS study. Mixed effect regression models were used to examine associations between students' BMI classification and weight perceptions and their math and English/French course grades. All models were stratified by sex and adjusted for sociodemographic covariates and school clustering. RESULTS: For English/French grades, males and females with overweight or underweight perceptions were less likely to achieve higher grades than their peers with perceptions of being at "about the right weight", controlling for BMI and covariates. For math grades, females with overweight perceptions, and all students with underweight perceptions, were less likely to achieve higher grades than their peers with "about the right weight" perceptions. All students with BMIs in the obesity range were less likely to report grades of 60% or higher than their peers with "normal-weight" BMIs, controlling for weight perception and covariates. Overweight BMIs were predictive of lower achievement in females for English/French grades, and in males for math grades, relative to "normal-weight" BMIs. Results for students that did not respond to the weight and weight perception items resembled those for obesity BMI and overweight/underweight perceptions, respectively. CONCLUSIONS: Overall, this study demonstrates that an obesity achievement gap remains when controlling for students' perceptions of their weight, and that both underweight and overweight perceptions predict lower academic performance, regardless of BMI classification. Results suggest barriers to academic success exist among youth with larger body sizes, and those with perceptions of deviating from "about the right weight".
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".