Identifiable Dietary Patterns of Pregnant Women: A Canadian Sample
Bibliographic record
Abstract
Purpose: To estimate the percentage of a sample of pregnant women in Canada following a vegetarian, vegan, low-carbohydrate, gluten-free, Mediterranean, or well-balanced diet, before and during pregnancy and to explore if pregnant women received and were satisfied with nutrition information received from health care providers (HCPs). Methods: Participants were conveniently sampled through Facebook and Twitter. An online survey collected data on sociodemographic characteristics, maternal diet, and whether women received and were satisfied with nutrition information from their HCPs. The McNemar test assessed changes in the proportion of diets followed before and during pregnancy. Results: Of 226 women, most followed a well-balanced diet before (76.9%) and during (72.9%) pregnancy (p = 0.26). Vegetarian, gluten-free, vegan, and low-carbohydrate diets were the least followed diets before and during pregnancy (vegetarian: 7.6% vs 5.3%; gluten-free: 4.9% vs 4.0%; vegan: 2.7% vs 2.2%; low-carbohydrate:4.0% vs 0.4%). Overall, the number of women following restrictive diets before pregnancy was significantly reduced throughout pregnancy (19.1% vs 12.0%, p < 0.001). Only 52.0% of women received nutrition information from their primary HCP, and 35.6% were satisfied with the nutrition information received. Conclusions: Most women followed a well-balanced diet before and during pregnancy and approximately one-third were satisfied with the information received from HCPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".