The interaction between individual, social and environmental factors and their influence on dietary intake among adults in Toronto
Bibliographic record
Abstract
Abstract Health outcomes related to vegetable and fruit consumption are widely recognized in the literature. This study investigates how dietary intake is influenced by individual, social, and environmental factors in the Toronto Census Metropolitan Area. The analysis and findings are based on data from the Canadian Community Health Survey which provides self‐reported vegetable and fruit intake from 6,513 adults in 2009‐2010. Food environment measures were constructed from commercial databases using kernel density estimates and network drive times. Spatial and multivariable techniques were used to determine the associations between diet, the food environment, and other health and socioeconomic factors. Particular emphasis was given to understanding the interaction between the food environment and socioeconomic position. Unexpectedly, supermarket density was found to have an inverse association with vegetable and fruit intake. Interaction terms for individuals with low income and reduced mobility produced different responses in men and women, confirming that the influence of the food environment is not uniform for all subgroups.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".