Examining the diversity of ultra-processed food consumption and associated factors in Canadian adults
Bibliographic record
Abstract
Ultra-processed food (UPF) consumption is increasing globally at an unprecedented rate. We investigated UPF consumption among Canadian adults and associated sociodemographic and health-related factors. This study was a secondary analysis of the Foodbook study (2014–2015), which collected self-reported data on foods consumed by Canadians during a 7-day period. UPF diversity was assessed by summing the different types of UPFs consumed in the previous week to produce a diversity score. Descriptive statistics summarized UPF diversity among subgroups in Canada. Regression models identified significant associations between UPF diversity, body mass index (BMI), and sociodemographic variables. This study included 6062 participants, aged 18 years and older, representing 24.7 million Canadian adults. Almost all Canadian adults (99.0%) consumed UPFs at least once weekly. The most common UPFs consumed were chocolate, chips/pretzels, cold breakfast cereal, and fast foods. UPF diversity was greatest among men, young respondents, those with high income, and those with obesity. When controlling for potential confounders, UPF diversity for men and women was significantly associated with younger age and higher BMI; it was also associated with region for women. This study suggests UPF consumption in Canada varies across sociodemographic subgroups, but ultimately is pervasive. Further research examining potential health risks associated with UPF consumption is encouraged to inform Canadian interventions. Novelty: Almost all Canadians consume at least one type of ultra-processed food weekly. Nearly half or more Canadians consume chocolate, chips/pretzels, cold breakfast cereal, or fast food at least once weekly. Gender, age, and BMI are consistently associated with ultra-processed food diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".