Compliance with iron and folic acid supplementation and associated factors among pregnant women in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Anaemia is one of the world’s leading cause of disability and the most serious global health issues. Globally about 38% (32 million) pregnant women are anaemic, from which 46.3% (9.2 million) are in Africa. Methods: Works of articles from PubMed, Medline and Google Scholar journal data-base were considered. Entirely articles allied to compliance and determinants of AFA supplementation were captured. The authors used modified Newcastle-Ottawa quality appraisal rule for cross-sectional works to assess the excellence of the studies for consideration and, tracked preferred reporting items for systematic reviews and meta-analysis guideline. The pooled effect size was calculated with the review manager and compressive meta-analysis software. Results: Eighteen studies with a total of 6649 pregnant women were included for analysis. Compliance of IFA supplementation in pregnancy in Ethiopia was 46.1%. Women who had experienced counselling on IFAS were 1.16 times, OR:1.16, (95% CI, 0.54, 2.50), knowledge on IFAS were 3.20 times, OR:3.20, (95% CI, 1.31, 7.85), knowledge of anaemia were five times OR:5.10, (95% CI, 1.87, 13.94), fourth visit for ANC were 1.58 times OR:1.58, (95% CI, 0.59, 3.42) and early registration for ANC were three times OR: 3.19, (95% CI, 0.77, 13.26) more likely to have compliance with IFAS compared to their counterpart. Conclusions: There is low compliance of IFAS in different parts of Ethiopia. Lack of counselling on IFAS, knowledge of IFAS and anaemia, no fourth visit for ANC and timing in ANC registration were factors that hinder compliance of IFAS.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".