Dietary Change during Pregnancy and Women’s Reasons for Change
Bibliographic record
Abstract
Women often make dietary changes during pregnancy; however, dietary modifications and reasons for changes are not well studied. We aimed to describe the dietary changes made during pregnancy, describe reasons for dietary changes, and determine what changes aligned with recommendations. Pregnant women (n = 379) recruited to the Alberta Pregnancy Outcomes and Nutrition (APrON) study in 2009/2010 completed a questionnaire in which they described dietary changes made during pregnancy and reasons for those changes. Changes and reasons were coded into categories. Women commonly reported increasing their intake of milk products, fruit, and sweet items and commonly decreased or eliminated intake of caffeine, alcohol, and meats. Women frequently reduced intake of foods for the baby’s health and increased foods to satisfy cravings. Changes made commonly aligned with recommendations for caffeine, alcohol intake, food safety, milk and alternatives, and fruit. Changes contrary to recommendations were common for fish and meats. The dietary changes women make during pregnancy appear to reflect women’s efforts to balance physiological changes accompanying pregnancy with the desire for healthy pregnancy outcomes. Understanding the reasons behind dietary change during pregnancy will help researchers and health professionals design effective strategies and public health messages to promote healthier pregnancies.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".