Changes in fat but not fruit and vegetable intakes linked with body weight change in Mexican women immigrants in Quebec
Bibliographic record
Abstract
The objective of the present study was to identify dietary parameters for predicting body weight change (ΔBW) in Mexican-born women (Mexicans) following immigration to Quebec City, Canada. Methods: Changes in fruit (ΔF), vegetable (ΔV), fruit and vegetable (ΔFV), and fat (ΔFat) intake were assessed according to post-immigration periods (1-5 years, 6-10 years, 11-20 years) using a food frequency questionnaire (FFQ). Anthropometric measures were also conducted in 87 Mexicans (study group) and 88 native-born Quebecers (comparison group) aged 18-65 years. Associations were calculated using full and partial robust regression models adjusting for potential confounders (origin, education, income, age, length of residence in Quebec City). Results: There was no difference in ΔBW between the groups. Body weight (BW) increased significantly in both Mexican (5.5 ± 0.9 kg, P P β = 0.03, P = 0.003), but not correlated with origin, ΔF, or ΔV. ΔBW was negatively associated with education (β = –4.33, P = 0.007) and positively associated with length of residence (β = 0.3, P = 0.003). Partial models indicated ΔF (β = –1.35, P β = –1.04, P = 0.0001), and ΔFV (β = –2.27, P β = 0.16, P = 0.04) was positively associated with ΔFat. Conclusions: Change in body weight could be predicted by length of residence, education, and change in fat intake in Mexican immigrant women and native-born Quebecers whereas changes in fruit and vegetable intakes could be predicted by Mexican or Quebec origin.
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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".