The magnitude of preconception care utilization and associated factors among women in Ethiopia: systematic review and meta-analysis, 2024
Bibliographic record
Abstract
Nowadays globally a woman dies every two minutes, and a neonate dies even in every 12 s, and more than two-thirds of these maternal deaths are preventable. Preconception care is a continuum of maternity care which is one of the proven strategies to reduce not only maternal mortality and morbidity but also neonatal mortality and morbidity by improving women’s health. Therefore, this study aimed to assess the pooled magnitude of preconception care utilization and its associated factors among women in Ethiopia. Searching of studies done through PubMed, Medline, Embase, Cochrane Library, List of References, and Google Scholar. Duplication of records was removed through the Endnote software manager. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) godliness was adhered to. A Newcastle- Ottawa Scale (NOS) assessment tool was used to evaluate the quality of studies. Analysis was performed using a random effect model with STATA 17 version software. Egger’s weighted regression and I 2 test were used to evaluate publication bias and heterogeneity respectively. In this systematic review and meta-analysis, a total of 6,808 women from 14 primary studies were included. The overall pooled magnitude of preconception care utilization in Ethiopia was 26% (95% CI: 18, 34). Educational status (OR = 2.18, 95%CI 1.54, 2.82), adequate knowledge of women regarding preconception care (OR = 2.69, 95%CI 1.90, 3.48), pre-existing medical illness (OR = 4.79, 95%CI 2.61, 6.06), history of adverse pregnancy outcome (OR = 3.18, 95%CI 2.74, 3.62), and husband /partner support (OR = 3.26, 95%CI 1.55, 4.97) were the identified predictors of utilization of preconception care. The overall magnitude of preconception care utilization among Ethiopian women was significantly low. Educational status of women, knowledge of women regarding Preconception care, history of adverse birth outcome, pre-existing chronic medical illness, and either psychological or financial husband support were the identified predictors of preconception care utilization. Thus, healthcare organizations should work on strategies to improve the utilization of preconception care. Moreover, advocating for better women’s education, awareness creation, and encouraging male involvement is very essential to optimize service utilization. CRD42023492995 registered on 23/12/2023.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".