Policy outcomes of applying different nutrient profiling systems in recreational sports settings: the case for national harmonization in Canada
Bibliographic record
Abstract
OBJECTIVE: To assess agreement among three nutrient profiling systems used to evaluate the healthfulness of vending machine products in recreation and sport settings in three Canadian provinces. We also assessed whether the nutritional profile of vending machine items in recreation and sport facilities that were adhering to nutrition guidelines (implementers) was superior to that of facilities that were not (non-implementers). DESIGN: Trained research assistants audited the contents of vending machines. Three provincial nutrient profiling systems were used to classify items into each province's most, moderately and least healthy categories. Agreement among systems was assessed using weighted κ statistics. ANOVA assessed whether the average nutritional profile of vending machine items differed according to province and guideline implementation status. SETTING: Eighteen recreation and sport facilities in three Canadian provinces. One-half of facilities were implementing nutrition guidelines. SUBJECTS: Snacks (n 531) and beverages (n 618) within thirty-six vending machines were audited. RESULTS: Overall, the systems agreed that the majority of items belonged within their respective least healthy categories (66-69 %) and that few belonged within their most healthy categories (14-22 %). Agreement among profiling systems was moderate to good, with κ w values ranging from 0·49 to 0·69. Implementers offered fewer of the least healthy items (P<0·05) and these items had a better nutritional profile compared with items in non-implementing facilities. CONCLUSIONS: The policy outcomes of the three systems are likely to be similar, suggesting there may be scope to harmonize nutrient profiling systems at a national level to avoid unnecessary duplication and support food reformulation by industry.
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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 | 0.049 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".