Investigating reformulation in the Canadian food supply between 2017 and 2020 and its impact on food prices
Bibliographic record
Abstract
OBJECTIVE: This study examined the relationship between reformulation and food price in Canadian packaged foods and beverages between 2017 and 2020. DESIGN: 5774). Price change by food category and by retailer were compared using Wilcoxon signed-rank tests. The proportion of products with changes in calories and nutrient levels were determined, and mixed-effects models were used to examine the relationship between reformulation and price changes. The Food Standards Australia New Zealand (FSANZ) nutrient profiling model was applied to calculate nutritional quality scores, and mixed-effects models were used to assess if changes in nutritional quality score were associated with price changes. SETTING: Large grocery retailers by market share in Canada. PARTICIPANTS: Foods and beverages available in 2017 and 2020. RESULTS: Food price changes differed by retailer and by food category (e.g. increased in Bakery, Snacks, etc; decreased in Beverages, Miscellaneous, etc.). Nutrient reformulation was minimal and bidirectional with the highest proportion of products changing in sodium (17·8 %; 8·4 % increased and 9·4 % decreased). The relationship between nutrient reformulation and price change was insignificant for all nutrients overall and was not consistent across food categories. Average FSANZ score did not change (7·5 in both years). For Legumes and Combination dishes, improvements in nutritional quality were associated with a price decrease and increase, respectively. CONCLUSIONS: Stronger policies are required to incentivise reformulation in Canada. Results do not provide evidence of reformulation impacting food prices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".