Underestimating a serving size may lead to increased food consumption when using Canada’s Food Guide
Bibliographic record
Abstract
It is unclear whether Canadians accurately estimate serving sizes and the number of servings in their diet as intended by Canada's Food Guide (CFG). The objective of this study was to determine if participants can accurately quantify the size of 1 serving and the number of servings consumed per day. White, Black, South Asian, and East Asian adults (n = 145) estimated the quantity of food that constituted 1 CFG serving, and used CFG to estimate the number of servings that they consumed from their 24-h dietary recall. Participants estimated 1 serving size of vegetables and fruit (+43%) and grains (+55%) to be larger than CFG serving sizes (p ≤ 0.05); meat alternatives (-33%) and cheese (-31%) to be smaller than a CFG serving size (p ≤ 0.05); and chicken, carrots, and milk servings accurately (p > 0.05). Serving size estimates were positively correlated with the amount of food participants regularly consumed at 1 meal (p < 0.001). From their food records, all ethnicities estimated that they consumed fewer servings of vegetables and fruit (-15%), grains (-28%), and meat and alternatives (-14%) than they actually consumed, and more servings of milk and alternatives (+26%, p ≤ 0.05) than they actually consumed. Consequently, 68% of participants believed they needed to increase consumption by greater than 200 kcal to meet CFG recommendations. In conclusion, estimating serving sizes to be larger than what is defined by CFG may inadvertently lead to estimating that fewer servings were consumed and overeating if Canadians follow CFG recommendations without guidance. Thus, revision to CFG or greater public education regarding the dietary guidelines is warranted.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".