Breastfeeding rates in immigrant and non‐immigrant women: A systematic review and meta‐analysis
Bibliographic record
Abstract
Breastfeeding benefits mothers and infants. Although immigration in many regions has increased in the last three decades, it is unknown whether immigrant women have better breastfeeding outcomes than non-immigrants. The aim of this study was to conduct a systematic review and meta-analysis to determine whether breastfeeding rates differ between immigrant and non-immigrant women. We searched Medline, Embase, PsycINFO, CINAHL and Google Scholar, 1950 to 2016. We included peer-reviewed cross-sectional and cohort studies of women aged ≥16 years that assessed and compared breastfeeding rates in immigrant and non-immigrant women. Two independent reviewers extracted data using predefined standard procedures. The analysis included 29 studies representing 1,539,659 women from 14 countries. Immigrant women were more likely than non-immigrants to initiate any (exclusive or partial) breastfeeding (pooled adjusted prevalence ratio 1.13, 95% confidence interval [CI] 1.07-1.19; 11 studies). Exclusive breastfeeding initiation was higher but borderline significant (adjusted prevalence ratio 1.20, 95% CI 1.00-1.45; 5 studies, p = 0.056). Immigrant women were more likely than non-immigrants to continue any breastfeeding between 12- and 24-week postpartum (pooled adjusted risk ratio 2.04, 95% CI 1.79-2.32; 3 studies) and > 24 weeks (adjusted risk ratio 1.33, 95% CI 1.02-1.73; 6 studies) but not exclusive breastfeeding. Immigrant women are more likely than non-immigrants to initiate and maintain any breastfeeding, but exclusive breastfeeding remains a challenge for both immigrants and non-immigrants. Social and cultural factors need to be considered to understand the extent to which immigrant status is an independent predictor of positive breastfeeding practices.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".