Maternal Dietary Diversity and Infant Outcome of Pregnant Women in Northern Ghana
Bibliographic record
Abstract
Objective: Little is known regarding the role of maternal dietary diversity score (DDS) in predicting poor outcomes of pregnancy including preterm delivery, and low birth weight (LBW). The main aim of this study was to explore the relationship between dietary diversity scores of urban Ghanaian women and infant weight at birth. Methods: This analytical cross-sectional study comprised 524 pregnant women who delivered singleton babies. A Structured questionnaire was used to collect data on socio-demographic variables (e.g. educational status, age, maternal occupation, household wealth index), obstetric history (for example, gravidity, gestational weight gain), dietary intake, malarial infection and Sulphadoxine pyrimethamine (SP) uptake, blood pressure (BP), haemoglobin concentration (Hb), anthropometric measurements (e.g. weight of mother and new born baby). Results: This study showed that maternal dietary diversity as measured by individual dietary diversity score scores (IDDS) was a significant independent predictor for mean birth weight and LBW. Analysis of covariance (ANCOVA) showed there was a significant difference in adjusted mean birth weight between women on low and high diversified diets , F (1, 415) = 8.935, p = 0.003. The results further showed that maternal IDDS was negatively associated with the incidence of LBW (Adjusted OR = 0.43, 95% CI = 0.22–0.85, p = 0.014). Conclusion: In nutritional deprived populations, maternal diet in the third trimester appears to be an important determinant of LBW and that DDS can serve as useful predictive indicator of maternal nutrition during pregnancy and the likelihood of delivering LBW babies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".