Trends in sugary drinks in Canada, 2004 to 2015: a comparison of market sales and dietary intake data
Bibliographic record
Abstract
OBJECTIVE: The current study aimed to examine the correspondence between sales data and dietary recall data for sugary drinks in Canada. DESIGN: Repeat cross-sectional analysis of sales data for sugary drinks sold in Canada from 2004 to 2015 from two sources: GlobalData (GD) and Euromonitor (EM). Sugary drinks included ten beverage categories containing free sugars. Analyses examined sales volumes over time, with adjustment for population growth. National intake estimates were drawn from the 2004 and 2015 Canadian Community Health Survey (CCHS) Nutrition. SETTING: Canada. PARTICIPANTS: Not applicable. RESULTS: In 2015, daily per capita sugary drinks consumption was estimated as 356 ml (GD) and 443 ml (EM) from sales data sources, and as 277 ml from dietary recall data. Both sales data sources and dietary recall data indicated that per capita sugary drinks consumption decreased from 2004 to 2015, although the magnitude of this change differed: -23 % (GD), -17 % (EM) and -32 % (CCHS Nutrition). Market sales data showed similar trends among categories of sugary drinks, with decreases in sales of traditional beverage categories (e.g. carbonated soft drinks) and increases in novel categories (e.g. sugar-sweetened coffee). CONCLUSIONS: All data sources indicate a declining trend in sugary drinks consumption between 2004 and 2015, but with considerable differences in magnitude. Consumption estimates from sales data were substantially higher than estimates from dietary recall data, likely due to under-reporting of beverage intake through dietary recall and the inability of sales data to account for beverages sold but not consumed. Despite the observed decline, sugary drinks sales volumes remain high in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".