Social influences on sugar consumption during pregnancy
Bibliographic record
Abstract
Studies of dietary intake during pregnancy have shown that many women increase their sugar intake during this time, which may contribute to poor diet quality and excess weight gain. The purpose of this research is to investigate physiological, social, and environmental factors that influence sugar intake during pregnancy. Guided by the methodology of focused ethnography, a series of semi‐structured interviews (n=8) have been conducted with pregnant women of varying sugar intakes. Transcripts were analyzed using qualitative content analysis to inductively derive knowledge about sugar intake. Preliminary findings from this study that have reached saturation include three social influences on sugar intake behaviours. Women conveyed an attitude that the state of pregnancy is a license to indulge in foods that are usually limited or avoided. Fathers could have either a positive or negative effect on the sugar intakes of women depending on their own dietary habits and views. Furthermore, social expectations of nutrition in pregnancy affected people's behaviours towards pregnant women and influenced eating behaviours during pregnancy. Strategies to facilitate healthier diets during pregnancy may include altering women's beliefs and attitude towards food and weight gain, including the father in nutritional counseling, and public health campaigns to increase knowledge of nutrition during pregnancy.
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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 | 0.003 |
| 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 | 0.000 |
| 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".