Postnatal care service utilization and associated factors in Ethiopia: A systematic review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: In Ethiopia, literatures are not in agreement on magnitude of utilization of postnatal care service and factors are not well identified. Hence, this systematic review and meta-analysis aimed to engender pooled evidence of magnitude of postnatal care service utilization and associated factors. Methods: For this systematic review and meta-analysis international databases such as, HINARI, PubMed, Medline, EMBASE, Google scholar and Cochrane library were used for searching studies published from 2002 to 2018 in English language. The Modified Newcastle- Ottawa quality assessment scale was used to assess the quality of studies and meta-analysis was conducted using STATA version 14. Q statistics and I2 test were used to assess the heterogeneity. The random effect model was used to estimate the pooled utilization of postnatal care at 95%CI. Result: Total of 22 articles with total samples of 42,320 women were included to this review. The pooled prevalence of postnatal care service utilization in Ethiopia was 41.5% (95%CI: 28.51-54.52). Women who attended formal education (OR, 2.37, 95%CI: 1.13, 4.97), had middle level and above household’s wealth quintile (OR, 2.57, 95%CI: 1.31,5.05), had antenatal care visit (OR, 6.72, 95%CI: 4.16, 10.87), being multigravida (OR, 0.68, 95%CI: 0.53, 0.88) and gave birth in health institutions (OR, 3.86, 95%CI: 2.89, 5.16) were high likely to utilize postnatal care services while rural resident women (OR, 0.17, 95%CI: 0.05, 0.60) were less likely to utilize postnatal care services. Conclusion: Postnatal care service utilization in Ethiopia is low (41.5%). It is affected by educational status, residence, wealth quintile, gravidity, antenatal care follow up and place of delivery. Scale up the service and awareness for rural people, improving economic status of the household, improve antenatal care follow up, and promote institutional delivery are having paramount importance. Key words: Health care service utilization, Ethiopia, systematic review, Meta-analysis
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".