Eating Behaviors and Dietary Patterns of Women during Pregnancy: Optimizing the Universal ‘Teachable Moment’
Bibliographic record
Abstract
Understanding women’s perceptions of eating behaviors and dietary patterns can inform the ‘teachable moment’ model of pregnancy. Our objectives were to describe eating behaviors and dietary patterns in pregnancy. This was a cross-sectional, national electronic survey. Women were ≥18 years of age, living in the United States, currently pregnant or less than two years postpartum, and had internet access. Age, education, race, and marriage were included as covariates in ordinal and binary logistic regressions (significance p < 0.05). Women (n = 587 eligible) made positive or negative changes to their diets, while others maintained pre-existing eating behaviors. The majority of women did not try (84.9 to 95.1% across diets) and were unwilling to try (66.6 to 81%) specific dietary patterns during pregnancy. Concerns included not eating a balanced diet (60.1 to 65.9%), difficulty in implementation without family (63.2 to 64.8%), and expense (58.7 to 60.1%). Helpful strategies included being provided all meals and snacks (88.1 to 90.6%) and periodic consultations with a dietitian or nutritionist (85 to 86.7%). Responses differed across subgroups of parity, body mass index, and trimester, notably in women with obesity who reported healthier changes to their diet (p < 0.05). Our study underscores the importance of tailoring care early to individual needs, characteristics, and circumstances.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".