Proximal and distal factors predicting timely initiation of breast feeding in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background In Ethiopia, the current coverage of timely initiation of breast feeding (TIBF) has fallen short of the national Health Sector Transformation Plan 2016-2020, National Nutrition Program 2016–2020 and WHO global target. This calls for the need to assess relevant proximal and distal factors that affect the rate of TIBF in Ethiopia. Objective The aim of this meta-analysis was to investigate the association between TIBF and educational status, household income, marital status, media exposure, and parity in Ethiopia. Methods Databases used were PubMed, EMBASE, Web of Science, SCOPUS, CINAHL and WHO Global health library, and key terms were searched using interactive searching syntax. It was also supplemented by manual searching. Observational studies published between September 2000 and March 2019 were included. The methodological quality of studies was examined using the Newcastle-Ottawa Scale (NOS) for cross-sectional studies. Data were extracted using the Joanna Briggs Institute (JBI) data extraction tool. To obtain the pooled odds ratio (OR), extracted data were fitted in a random-effects meta-analysis model. Statistical heterogeneity was quantified using Cochran’s Q test, τ 2 , and I 2 statistics. Additional analysis conducted includes Jackknife sensitivity analysis, cumulative meta-analysis, and meta-regression analysis. Results Out of 553 studies retrieved, 25 studies fulfilled our inclusion criteria. Almost all studies were conducted on mothers with newborn less than 23 months. Maternal educational status (OR = 1.82; p < 0.001; 95% CI = 1.35 - 2.45; I 2 = 84.96%), paternal educational status (OR = 2.72; p = 0.001, 95% CI = 1.49 - 4.97 I 2 = 62.50%), income (OR = 1.16; p = 0.002; 95% CI = 1.05 - 1.27; I 2 = 0.00%), marital status (OR = 1.39; p = 0.001; 95% CI = 1.14 - 1.69; I 2 = 9.17%) and parity (OR = 1.39; p = 0.01; 95% CI = 1.07 - 1.81; I 2 = 74.43%) were found to be significantly associated with TIBF. We also observed a direct dose-response relationship of TIBF with educational status and income. Conclusions Proximal and distal factors significantly predicting TIBF practice in Ethiopia, which needs integrated intervention by health professionals and healthcare policymakers. Health education, counselling and peer education targeting parents at antenatal and postnatal periods are needed. It is also relevant to improve the economic power of women and promote gender equality.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".