Body Image, Body Mass Index, and Body Composition: In Young Adults
Bibliographic record
Abstract
PURPOSE: Associations were examined between body image and body mass index (BMI) in comparison with body composition in healthy weight, overweight, and obese young adults. METHODS: Weight and height were determined, and the percentage of fat mass (%FM) and percentage of fat-free mass (%FFM) were measured by dual energy X-ray absorptiometry in 75 male and 87 female young adults (21.1 ± 1.9 years; 25.2 ± 4.4 kg/m² [mean ± standard deviation]). Body image was measured using the three subscales Weight Esteem, Appearance Esteem, and External Attribution of the Body-Esteem Scale for Adolescents and Adults (BESAA). RESULTS: Body mass index and %FM were highly correlated (r for males = 0.74, r for females = 0.82; both p<0.001), and were inversely associated with body image, particularly Weight Esteem. After adjustment for physical activity, BMI and %FM (and %FFM, although in the opposite direction) were associated with each BESAA subscale: %FM, %FFM, and BMI explained 12% to 14% of the variance in Appearance Esteem for both sexes, 33% to 41% in Weight Esteem in women and 16% to 18% in men, and 8% to 10% in External Attribution in women (all p<0.05) and <5% for men (NS). CONCLUSIONS: Clinicians should be aware that as their clients' BMI and %FM increase, body image decreases, particularly in women.
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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 | 0.000 | 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) | 0.001 | 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".