Complementary Feeding Practices for South Asian Young Children Living in High-Income Countries: A Systematic Review
Bibliographic record
Abstract
Sub-optimal nutrition among South Asian (SA) children living in high-income countries is a significant problem. High rates of obesity have been observed in this population, and differential complementary feeding practices (CFP) have been highlighted as a key influence. Our aim was to undertake a systematic review of studies assessing CFP in children under two years of age from SA communities living in high-income countries, including dietary diversity, timing, frequency and promotors/barriers. Searches covered January 1990⁻July 2018 using MEDLINE, EMBASE, Global Health, Web of Science, BanglaJOL, OVID Maternity and Infant Care, CINAHL, Cochrane Library, POPLINE and World Health Organisation (WHO) Global Health Library. Eligible studies were primary research on CFP in SA children aged 0⁻2 years. Search terms were "children", "feeding" and "South Asian", and derivatives. Quality appraisal used the Evidence for Policy and Practice Information (EPPI) Weight of Evidence scoring. From 50,713 studies, 13 were extracted with ten from the UK, and one each from the USA, Canada and Singapore. Sub-optimal CFP were found in all studies. All ten studies investigating timing reported complementary feeding (CF) being commenced before six months. Promoters/barriers influencing CFP included income, lack of knowledge, and incorrect advice. This is the first systematic review to evaluate CFP in SA children living in high-income countries and these findings should inform the development of effective interventions for SA infants in these settings.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".