Dietary diversity and associated factors among pregnant women in Ethiopia: a systematic review with meta-analysis
Bibliographic record
Abstract
Background: Dietary diversity (DD) is the consumption of a variety of different and healthy foods that promote an adequate supply of nutrients, a high-quality diet, and the maintenance of optimal health. It is critical to identify the factors that influence pregnant women's eating habits so that relevant interventions can be developed. We estimated the pooled odds ratio of appropriate dietary practices to identify factors that affect the dietary practices of pregnant women. Methods: statistic tests with corresponding P-values were used to determine the existence of heterogeneity between studies. Publication bias was tested using a funnel plot of symmetry and further investigated using Egger and Begg tests. The results were presented using forest plots, funnel plots, tables, and figures. Results: We included 29 articles with maximum and minimum sample sizes of 759 and 241, respectively. Among the included articles, 13 were facility-based cross-sectional studies;16 studies were community-based cross-sectional studies. The pooled proportion of adequate DD was 42.48% (95% confidence interval (CI) = 31.82, 53.14). Knowledge of DD (OR = 3.10; 95% CI = 1.92, 4.99), income (OR = 0.35; 95% CI = 0.14, 0.85), and nutritional information (OR = 1.91; 95% CI = 1.15, 3.17) were predictors for adequate DD practice among pregnant women. Conclusions: The pooled proportion of adequate DD among pregnant women was low. Knowledge of DD, household income, and nutritional information were associated factors with the adequate dietary diversity of pregnant women. We recommend focusing on interventions that will enhance the knowledge of dietary diversity through improved nutritional awareness and enhance access to food resources through existing maternal health initiatives. Registration: PROSPERO: CRD42022298172.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".