Herbal medicine use and predictors among pregnant women attending antenatal care in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The use of herbal medicine among pregnant women is increasing in many low- and high-income countries due to their cost-effectiveness in treatment and ease of access. Research findings across Ethiopia on the prevalence and predictors of herbal medicine use among pregnant women attending antenatal care are highly variable and inconsistent. Therefore, this systematic review and meta-analysis aims to estimate the overall prevalence of the use of herbal medicine and its predictors among pregnant women attending antenatal care in Ethiopia. METHOD: tests were used to assess heterogeneity. A random effect meta-analysis model was used to estimate the pooled prevalence. In addition, the association between risk factors and herbal medicine use in pregnant women attending antenatal care were examined. RESULTS: A total of eight studies were included in this review. The pooled prevalence of herbal medicine use among pregnant women attending antenatal care in Ethiopia was 47.77% (95% CI: 28.00-67.55). Subgroup analysis by geographic regions has showed that the highest prevalence (57.49%;95% CI: 53.14, 61.85) was observed in Oromia Region and the lowest prevalence was observed in Addis Ababa (31.39%; 95% CI: 2.83, 79.96). The herbal medicines commonly consumed by women during pregnancy were ginger: 41.11% (95% CI: 25.90, 56.32), damakasse: 34.63% (95% CI: 17.68, 51.58), garlic: 32.98% (95% CI: 22.21, 43.76), tenaadam: 19.59% (95% CI: 7.54, 31.63) and eucalyptus: 4.71% (95% CI: 1.1, 8.26). Mothers' previous history of self-medication (95% CI: 1.91, 51.35), illness during pregnancy (95% CI: 1.56, 23.91), employment status (95% CI: 3.89, 10.89), educational status (95% CI: 1.52, 2.68), and place of residence (95% CI: 1.86, 3.23) were predictors of herbal medicine use by women during pregnancy. CONCLUSION: In this study, about half of women attending antenatal care use herbal medicine and it is relatively high. The most commonly consumed herbal medicine during pregnancy was ginger followed by damakasse, garlic, tenaadam and eucalyptus. During pregnancy, it is not known that these most commonly consumed plant species have harmful fetal effects. However, many of the medicinal plant species are poorly studied, and it is not possible to rule out teratogenic effects. Teamwork between healthcare professionals and traditional practitioners to educate on the use of medicinal plants will encourage healthier pregnancies and better health for mothers and infants.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".