Modelling Food Substitution Using the Ofcom Nutrient Profiling Model on Population Intakes from the Canadian Community Health Survey–Nutrition 2015
Bibliographic record
Abstract
This study aimed to model how substituting foods consumed by Canadians for alternatives with more favourable nutrient profiling (NP) scores would impact dietary intakes. The Ofcom NP system, developed to help the UK Office of Communication differentiate foods that can be advertised to children, was applied to foods consumed by Canadians aged 2 years and older in the 2015 Canadian Community Health Survey (CCHS) (n = 19,447). Foods were substituted for similar options from the Euromonitor branded food composition database (Scenario 1) or from the primarily aggregated food profiles in the CCHS survey food composition database (Scenario 2) with either the most favourable (optimistic; 1A and 2A) or a more favourable Ofcom score (realistic; 1B and 2B). Mean intakes of Ofcom scores, calories, saturated fat, sugars, and sodium from these scenarios were compared to baseline. Only 2.9% of foods consumed had a similar Euromonitor option with a lower Ofcom score. Scenarios 1A, 1B, and 2A had lower Ofcom scores, calorie, sodium, saturated fat, and sugar intakes compared to baseline. Scenario 2B had lower levels of all outcome measures, except for an increase in calories compared to baseline. Selection of foods with more favourable NP scores has the potential to decrease the Canadian intake of nutrients of concern.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".