Learning From Pregnant Women Eating 5 Servings or More of Vegetables Daily: Strategies, Behaviors, and Motivators
Bibliographic record
Abstract
OBJECTIVE: To explore the context, behaviors, strategies, and motivators of pregnant women who consume 5 servings of vegetables daily. METHODS: Positive deviance study involving Australian pregnant women (9 of 529) identified through a validated food frequency questionnaire. Semistructured interviews explored their strategies, behaviors, and motivators. RESULTS: Women valued vegetables for health benefits and disease management. Prioritizing vegetables in main meals and snacks was key, supported by planning, purchasing, and preparation. Social support and a positive environment facilitated regular vegetable consumption easier. Cooking skills and, in some cases, gardening were important enablers. Results provide practical strategies to address commonly reported challenges to vegetable consumption. CONCLUSIONS AND IMPLICATIONS: Pregnant women's experiences of meeting vegetable intake recommendations offer valuable insights into practices that enhance dietary quality. Further research and testing in practice is warranted with pregnant women and their significant others to promote increased vegetable intake and better outcomes for families.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".