Intakes of Total, Free, and Naturally Occurring Sugars in the French-Speaking Adult Population of the Province of Québec, Canada: The PREDISE Study
Bibliographic record
Abstract
= 1147, 18-65 years old; 50.2% women). Because only total sugar content of foods and beverages is available in the Canadian Nutrient File (CNF) 2015, the initial step of this study was thus to build a database of free and naturally occurring sugars content of each food item and recipe included in the R24W, which is an automated, self-administered, web-based, 24-h dietary recall validated to estimate nutrient intakes in French-speaking adults of the Province of Québec. Total sugars were manually differentiated into free and naturally occurring sugars using a systematic algorithm based on previously published systematic algorithms. The World Health Organization (WHO)'s free sugar definition was used to differentiate total sugars into free and naturally occurring sugars. Dietary intake estimates were assessed using three 24-h dietary recalls completed with the R24W. Mean total, free, and naturally occurring sugar intakes were 116.4 g (19.3% of daily energy intake (%E)), 72.5 g (11.7%E), and 44.0 g (7.5%E), respectively. Over half (57.3%) of the overall sample did not meet the WHO's recommendation to consume less than 10%E from free sugars. Women had a higher %E from naturally occurring sugars than men and being younger was associated with a greater %E from free sugars. Sugar intakes among French-speaking adults from the Province of Québec were mainly in the form of free sugars, with the majority of the population exceeding the WHO recommendation regarding free sugar intake. This suggests that public health efforts towards reducing free sugar intake in this population are relevant and necessary, considering that overconsumption of free sugars negatively influences health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".