Association of age and colostrum discarding with breast-feeding practice in Ethiopia: systematic review and meta-analyses
Bibliographic record
Abstract
OBJECTIVE: To investigate whether maternal/caregiver's age, infant age (0-6 months) and discarding colostrum affects timely initiation of breast-feeding (TIBF) and exclusive breast-feeding (EBF) in Ethiopia. DESIGN: A systematic search of PubMed, SCOPUS, EMBASE, CINHAL, Web of Science and WHO Global Health Library electronic databases was done for all articles published in English from 2000 to January 2018. Two reviewers independently screened, extracted and graded the quality of studies using Newcastle-Ottawa Scale. A weighted inverse-variance random-effects model meta-analysis, cumulative meta-analysis and mixed-effects meta-regression analysis were done. SETTING: All observational studies conducted in Ethiopia.ParticipantsMothers of children aged less than 2 years.ResultA total of forty articles (fourteen studies on TIBF and twenty-six on EBF) were included. TIBF was associated with colostrum discarding (OR=0·38; 95 % CI 0·21, 0·68) but not with maternal/caregiver's age (OR=0·98; 95 % CI 0·83, 1·15). In addition, colostrum discarding (OR=0·53; 95 % CI 0·36, 0·78) and infant age (OR=1·77; 95 % CI 1·38, 2·27) were significantly associated with EBF but not maternal/caregiver's age (OR=1·09; 95 % CI 0·84, 1·41). CONCLUSIONS: There was no association between maternal/caregiver's age and breast-feeding practice (EBF and TIBF). Colostrum discarding was associated with both EBF and TIBF. This evidence could be helpful to counsel all mothers of reproductive age and who discard colostrum.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 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".