Dietary Changes Albertan Women Make During Pregnancy: Thematic Analysis of Self-reported Changes and Reasons
Bibliographic record
Abstract
PURPOSE: To explore dietary changes Albertan women make during pregnancy, reasons they make changes, and alignment with prenatal nutrition recommendations. METHODS: Women up to 6 months postpartum were recruited in public health centres and Primary Care Networks. Qualitative data were collected through a self-administered survey including 2 open-ended questions that asked about changes made to food/beverage intake during pregnancy and why these changes were made. RESULTS: A majority (n = 577) of the 737 women completing the survey described changes they made to their food/beverage intake during pregnancy and 193 respondents provided reasons for these changes. Increased intake of fruits/vegetables, meat, milk, and their alternatives (n = 600); limiting or avoidance of foods/beverages known to be harmful during pregnancy (n = 445); and increased food/fluid intake or meal/snack frequency (n = 405) were commonly reported dietary changes. Motivations relating to health and to control physiological changes/manage health conditions were the most frequent reasons provided. CONCLUSIONS: Women make diverse dietary changes and have various motivations for food choices during pregnancy. A majority make dietary changes to support a healthy pregnancy. However, the motivation to control discomforts and respond to hunger and thirst sensations reflect a stronger influencer on women's choices than is currently addressed in prenatal nutrition messages.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".