Applicability of the Socioecological Model for Understanding and Reducing Consumption of Ultra-Processed Foods in Canada
Bibliographic record
Abstract
Ultra-processed foods (UPFs) have become a major contributor to the diets of Canadians, with a recent report from Statistics Canada suggesting Canadians are consuming almost one-half of their calories from UPFs. Research has linked UPF consumption with increased risk for chronic diseases such as cardiovascular disease and type 2 diabetes, among others. This paper sought to investigate the popularity of UPFs, particularly among children and teens, utilizing the socioecological model as a framework to illustrate how influences at multiple levels (i.e., public policy, organizational, community, interpersonal, and individual) have played a role in the proliferation of UPFs. Evidence from previous studies is used to identify how factors at different levels may influence UPF consumption and discuss potential strategies for reducing UPF consumption. To meaningfully reduce UPF consumption among Canadians, all levels should be considered, with the goal of creating a healthier Canadian population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".