Adherence to Canada’s Food Guide Recommendations during Pregnancy
Bibliographic record
Abstract
In Canada, pregnant women are typically referred to Canada’s Food Guide (CFG), a set of national dietary recommendations designed to promote adequate nutrient intake. Pregnant women are also advised to gain weight within the Institute of Medicine guidelines, which differ by prepregnancy body mass index (BMI). However, CFG recommendations do not account for prepregnancy BMI and provide no guidance on “less healthy” (LH) foods. The aim of this study was to score women’s diets according to adherence to CFG recommendations and consumption of LH foods and to examine differences between these diet scores by prepregnancy BMI. Participants enrolled in the APrON (Alberta Pregnancy Outcomes and Nutrition) prospective cohort study completed a 24-h recall in their second trimester (n = 1630). A score was created on the basis of each daily dietary CFG recommendation met, ranging from 0 to 9. The distribution of consumption (grams per day) of 8 LH food groups was given a score of 0 (none) or 1, 2, or 3 (representing the lowest, middle, or highest tertiles, respectively) and summed giving a total LH score of 0–24. There were few differences in CFG recommendations met by prepregnancy BMI status, although fewer women who were overweight or obese prepregnancy met the specific recommendation to consume 7–8 servings of fruit or vegetables/d than did those who were under- or normal weight (47% and 41% compared with 50% and 54%, respectively). Although differences were small, women who were obese prepregnancy had lower CFG scores (β = −0.28; 95% CI:−0.53, −0.02) and higher LH scores (β = 0.45; 95% CI: 0.04, 0.86) than did those who were normal weight. The study results suggest that more attention may need to be paid to individualized counseling on dietary recommendations that take account of prepregnancy BMI.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".