Determinants of dietary patterns and diet quality during pregnancy: a systematic review with narrative synthesis
Bibliographic record
Abstract
OBJECTIVE: To identify determinants of diet in pregnancy, by detecting factors in our multiple-determinants life course framework that are associated with dietary patterns, quality or guideline adherence. DESIGN: A systematic review of observational studies, published in English or German, was conducted. Sociodemographic, lifestyle, environmental and pregnancy-related determinants were considered. Four electronic databases were searched in January 2015 and updated in April 2016 and a total of 4368 articles identified. Risk of bias was assessed using adapted Newcastle-Ottawa Scales. SETTING: High- and upper-middle-income countries. SUBJECTS: Pregnant or postpartum women reporting their dietary intake during pregnancy. RESULTS: Seventeen publications of twelve studies were included and compared narratively due to heterogeneity. Diet in pregnancy was patterned along a social gradient and aligned with other health behaviours before and during pregnancy. Few studies investigated the influence of the social and built environment and their findings were inconsistent. Except for parity, pregnancy determinants were rarely assessed even though pregnancy is a physiologically and psychologically unique period. Various less well-researched factors such as the role of ethnicity, pregnancy intendedness, pregnancy ailments and macro-level environment were identified that need to be studied in more detail. CONCLUSIONS: The framework was supported by the literature identified, but more research of sound methodology is needed in order to conclusively disentangle the interplay of the different determinants. Practitioners should be aware that pregnant women who are young, have a low education or do not follow general health advice appear to be at higher risk of inadequate dietary intake.
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".