Sex and racial/ethnic differences in the prevalence of overweight and obesity among U.S. college students, 2011–2015
Bibliographic record
Abstract
Objective To investigate sex and racial/ethnic differences in overweight and obesity in college students. Participants: A nationally representative sample of 319,342 U.S. college students (mean age = 20.4 years; 67.7% female) from Fall 2011 to Spring 2015. Methods: A secondary data analysis of multi-year cross-sectional data was performed. Multiple logistic regression was used to examine factors (e.g. cumulative grade average, year in school, and living place) associated with overweight and obesity determined from BMI calculated by self-reported height and weight. Results: The prevalence of overweight and obesity was significantly higher for both sexes in Spring 2015 than in Fall 2011. Significant differences were found in overweight and obesity by sex and race/ethnicity. Higher adjusted odds ratios for overweight and obesity were observed for men, blacks, and Hispanics (compared to whites). Asians had the lowest adjusted odds of overweight and obesity. Conclusions: Intervention strategies for the prevention and management of overweight and obesity in U.S. college students should consider sex and racial/ethnic inequalities.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".