Who is consuming ultra-processed food in Canada? A cross-sectional analysis of the 2018/2019 International Food Policy Study
Bibliographic record
Abstract
High consumption of ultra-processed foods and drinks (UPF) has been linked to poor diet quality and an increased risk of non-communicable diseases. To inform public policies and interventions aimed at reducing UPF intake in Canada, updated information on UPF intake among different sociodemographic groups is needed. This study, using data from 5872 adults aged 18 years and older from the International Food Policy Study (2018–2019), aims to estimate the dietary energy contribution of UPF and assess its variation among sociodemographic subgroups. All foods and drinks reported in a single 24 h dietary recall were classified using the Nova system. We estimated the mean proportion of total daily energy from UPF and subcategories of UPF in the overall sample and among sociodemographic subgroups. Multivariable linear regression models evaluated the association between sociodemographic characteristics with the proportion of total daily energy from UPF. On average, adults consumed 45.2% of their total daily energy from UPF. UPF consumption was slightly higher among males than females (49.4% vs. 47.6%, p = 0.039) and younger adults aged 19–30 years compared with older adults aged 51–64 years (50.0% vs. 47.2%, p = 0.029), adjusting for a range of sociodemographic factors. Overall, UPF consumption was relatively high among adults in all sociodemographic subgroups, highlighting the need for policies to decrease UPF consumption in the entire 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".