Correlates of diet quality in the Quebec population
Bibliographic record
Abstract
OBJECTIVE: To determine the correlates of a high-fat diet in urban, suburban and rural areas of Quebec, Canada. DESIGN: A secondary analysis of data collected as part of a 5-year multi-factorial, multi-setting, community-intervention project. SETTING: Urban, suburban and rural settings of the province of Quebec, 1997. SUBJECTS: Data were analysed from a sample of 5214 participants (2227 males, 2987 females). A food-frequency questionnaire was completed and a global index of food quality was calculated. Logistic regression was used to identify correlates of a diet high in total fats, saturated fat and cholesterol. RESULTS: In both genders, lower level of education, smoking status, French and English languages compared with other languages spoken at home, and a rural environment were associated with poor diet quality. Having no intention to eat low-fat dairy products more often was associated with a high-fat diet. In men, obesity (body mass index >/=30 kg m(-2)) and absence of reported health problems were correlates of a high-fat diet, while, in women, lower physical activity was a correlate. CONCLUSIONS: Future health interventions in Quebec should target people with low education, smokers and those living in a rural environment. Obese men and sedentary women should have access to specific dietetic resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".