3.3-O3Birthweight of babies born to migrant mothers - what role does destination country play?
Bibliographic record
Abstract
Background: A baby’s birthweight (BW) is determined by numerous factors, including maternal ethnic/geographic origin and current living conditions. Studying migrant mothers originating from the same region, but living in different destination countries, may indicate whether integration policies affect birth outcomes. We aimed to determine BW disparities among babies born to women according to the maternal region of origin and integration policies of the destination country. Methods: We used pooled data for more than 30 million singleton term births between 1998-2014. The Migrant Integration Policy Index (MIPEX) country score 2010 was used to indicate policies for integration in destination countries: Denmark, Japan, Spain (low MIPEX), Belgium, Canada, Finland, Norway, Scotland and Sweden (high MIPEX). BW differences in grams (g) were analysed with regression analysis for aggregate continuous data, adjusting for maternal age, parity and year of birth. Results: Babies of East Asian mothers had the highest BW if born in Sweden (3524g; 95% confidence interval (CI) 3518-3529) and lowest in Japan (3222g; 95% CI 3217-3226). Amongst Latin-American women, babies were heaviest in Sweden (3583g) and lightest in Spain (3229g). The pattern was similar in Sub-Saharan, South Asian, North American and European mothers, with the heaviest babies being born in countries with high MIPEX score. MIPEX score explained 8-38% of birthweight variations, depending on maternal region of origin. Conclusions: Favourable integration policies in the destination country are associated to higher infant birthweight among women originating from the same world region; however, this varies according to maternal origin. Main message: Using data from 30 million births from high-income countries, we found that the birthweight of babies born to migrant mothers from the same geographical region was consistently higher in destination countries with favourable migrant integration policies, compared to destination countries with less favourable policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".