The Amount and Sources of Sugar Intake in Canadian Adults
Bibliographic record
Abstract
The objective of the study was to determine the sugar intakes of Canadians and identify the top food sources contributing to sugar intake, so as to gain an understanding of the sugar consumption pattern of Canadians. The web‐based Canadian Diet History Questionnaire, which is a validated food frequency questionnaire assessing intake over the past month, was administered in June 2012, to a nationally representative sample of 1,833 (aged 18 years and older) Canadian adults. The estimated mean sugar intake was 60g/day, which was 15% of total energy intake. Compared to the DRI recommendation of 25% or less of total energy intake coming from sugars, 15% is considered a moderate amount. The results showed that 87% of Canadian adults consumed <= 100 g of sugar per day. Males (102g/d) consume significantly more sugars then females (82g/d) (p<0.05). Almost half (47%) of the average daily sugar intake of Canadian adults came from beverages, specifically fruit juices and drinks (24%), soda beverages (12%) and milk (11%). Fruit (10%) was another source of high sugar intake. These results provide an assessment of the sugar consumption pattern of Canadians and indicate the need for communication strategies that inform Canadian consumers on how to reduce their sugar intake. Considering that reducing sugar consumption is a stated priority of the World Health Organization, implementation of changes are required to nutrition labelling to better help consumers identify the food sources and amounts of sugars, particularly in beverages. Including a %Daily Value for total sugars would provide a benchmark to help consumers identify foods that contain high amounts of sugar Funding Sources: UofT McHenry Chair research fund (ML); Dairy Farmers of Canada (ML); CIHR Public Health Policy Fellowship (MA).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".