Dietary Intake among Grade 7 Students from Southwestern Ontario Attempting to Gain Weight
Bibliographic record
Abstract
PURPOSE: To describe dietary intake associated with intentional weight gain among grade 7 students. METHODS: Data were collected using the Waterloo web-based Eating Behaviour Questionnaire (WEB-Q) and measured heights/weights were taken to assess Body Mass Index (BMI). Dietary intake and the Canadian Healthy Eating Index-2009 were compared among participants who ate more to gain weight. RESULTS: Among 1015 participants, approximately 9% of participants were actively attempting to gain weight with more males than females (P < 0.001) and more underweight and normal weight than overweight/obese (P < 0.001) participants. Unadjusted analyses revealed that weight gainers versus non-weight gainers consumed more grain products (P < 0.001), meat and alternatives (P = 0.005), and other foods (P < 0.001), in addition to more total energy (P < 0.001). Although greater amounts of carbohydrates, fat, and protein were consumed among the weight gainers, no differences in the percentage of each macronutrient were observed once corrected for total energy intake. The adjusted model revealed that weight gainers were more likely to consume grain products in line with current recommendations, yet they were further from the recommendations for total fat intake. CONCLUSION: Health promotion strategies need to consider intentional weight gain among young adolescents to ensure that appropriate weight gaining strategies are being followed to avoid potential detrimental health effects.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".