Influence of paternal and maternal ethnicity and ethnic enclaves on newborn weight
Bibliographic record
Abstract
BACKGROUND: The association between maternal ethnicity and newborn weight is understood. Less is known about the additional influence of paternal ethnicity and neighbourhood ethnic composition. METHODS: We studied 692 301 singleton live births of parents of Canadian, Bangladeshi, Sri Lankan, Pakistani, Indian, Filipino, Vietnamese, Korean, Hong Kong or Chinese birthplace. We used multivariable regression to calculate mean (95% CI) birthweight differences between infants of two Canadian-origin parents and (1) foreign-born mother and Canadian-born father, (2) Canadian-born mother and foreign-born father or (3) two foreign-born parents from the same country. We also stratified by high versus low same-ethnic concentration of the parent's residence. We adjusted for gestational age at birth, maternal age, parity, marital status and income quintile. RESULTS: Compared with male and female infants of two Canadian-born parents, those of same-country foreign-born parents weighed 6.2% (-218 g, 95% CI -214 g to -223 g) and 5.6% (-192 g, 95% CI -187 g to -196 g) less, respectively. The largest mean weight difference was among male (8.4% (-297 g, 95% CI -276 g to -319 g)) and female (8.2% (-279 g, 95% CI -262 g to -296 g)) infants of two Bangladeshi parents. Infants of a foreign-born mother and Canadian-born father had weights closest to those of two Canadian-born parents. Residing in an area of high (vs low) same-ethnic concentration was associated with lower birthweight among infants of mixed union couples, but not among those of parents originating from the same country. CONCLUSIONS: Paternal and maternal ethnic origin influence newborn weight, which is modified by settlement in a high same-ethnic concentration area only among parents of mixed union.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".