Body mass index, eating attitudes and behaviors, body image and physical activity in young Caucasian and Asian Canadian women
Bibliographic record
Abstract
The effect of ethnicity on associations among Body Mass Index (BMI), eating attitudes, body image and physical activity is not straightforward, and few studies have assessed differences between Caucasians and Asians. To explore this, we recruited 51 Caucasian and 52 Asian (Chinese) healthy female university students living in Vancouver. Height and weight were measured and body fat assessed by dual‐energy x‐ray absorptiometry. Questionnaires were used to assess eating attitudes, body image, physical activity and energy intake (food frequency questionnaire). Compared to Asians, Caucasian women had similar energy intake (1778±633 vs 1759±713 kcal) and % body fat (28.3±6.4% vs 27.3±5.3%) but higher BMI (22.4±2.6 vs 20.9±2.0 kg/m 2 , p<0.01). Caucasians were more active (Baecke physical activity score 9.0±1.6 vs 7.5±1.5, p<0.001) and were more likely to report a current weight loss effort (46% vs 23%, p<0.05). However, Asian women had significantly higher scores (indicating greater dissatisfaction or more disordered eating attitudes) on the Beliefs About Appearance Scale (44.2±13.9 vs 38.5±13.6) and the Eating Disorders Inventory subscales for bulimia (13.8±4.5 vs 11.7±2.9), drive for thinness (19.8±6.6 vs 16.4±6.1) and body dissatisfaction (31.6±7.7 vs 27.4±10.0). With BMI as a covariate the size of these differences increased, and differences in scores on the Body Shape Questionnaire and the cognitive dietary restraint subscale of the Three‐Factor Eating Questionnaire became significant. Despite lower relative weight among Asian Canadian women, eating‐ and body image‐related concerns are common. Supported by CIHR MOP 79563.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".