Community and International Nutrition Fruit and Vegetable Consumption Is Lower and Saturated Fat Intake Is Higher among Canadians Reporting Smoking1
Bibliographic record
Abstract
ABSTRACT Understanding differences in dietary patterns by smoking status is important for nutritionists and health educators involved in helping individuals to make healthy dietary and lifestyle choices. Although smokers have a poor quality diet compared with nonsmokers, no study has examined nutritional adequacy and variability in the nutrient intake of smokers. The aim of this study was to compare dietary habits of smokers with nonsmokers in terms of nutrient intake, food groups contributing to nutrient intake, nutritional adequacy and day-to-day variation in nutrient intake. Noninstitutionalized adults aged 18–65 y (n 5 1543) who participated in the Food Habits of Canadians Survey (1997–1998) were studied. Subjects, selected from across Canada using a multistage, random-sampling strategy, completed an in-home 24-h dietary recall. Repeat interviews were conducted in a subsample to estimate variability in nutrient intake. Smokers had higher intakes of total and saturated fat, and lower intakes of folate, vitamin C and fiber than nonsmokers. There were no significant differences in calcium, zinc and vitamin A intakes or day-to-day variation in nutrient intake by smoking status. Smokers consumed significantly fewer fruits and vegetables than nonsmokers, leading to lower intakes of folate and vitamin C. In conclusion, smokers have a less healthy diet than nonsmokers, placing them at higher risk for chronic disease as a result of both dietary and smoking habits. Diet may act as a confounder in smoking-disease relationships. J. Nutr. 131: 1952–1958, 2001.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".