Birth Outcomes of Latin Americans in Two Countries with Contrasting Immigration Admission Policies: Canada and Spain
Bibliographic record
Abstract
BACKGROUND: We delved into the selective migration hypothesis on health by comparing birth outcomes of Latin American immigrants giving birth in two receiving countries with dissimilar immigration admission policies: Canada and Spain. We hypothesized that a stronger immigrant selection in Canada will reflect more favourable outcomes among Latin Americans giving birth in Canada than among their counterparts giving birth in Spain. MATERIALS AND METHODS: We conducted a cross-sectional bi-national comparative study. We analyzed birth data of singleton infants born in Canada (2000-2005) (N = 31,767) and Spain (1998-2007) (N = 150,405) to mothers born in Spanish-speaking Latin American countries. We compared mean birthweight at 37-41 weeks gestation, and low birthweight and preterm birth rates between Latin American immigrants to Canada vs. Spain. Regression analysis for aggregate data was used to obtain Odds Ratios and Mean birthweight differences adjusted for infant sex, maternal age, parity, marital status, and father born in same source country. RESULTS: Latin American women in Canada had heavier newborns than their same-country counterparts giving birth in Spain, overall [adjusted mean birthweight difference: 101 grams; 95% confidence interval (CI): 98, 104], and within each maternal country of origin. Latin American women in Canada had fewer low birthweight and preterm infants than those giving birth in Spain [adjusted Odds Ratio: 0.88; 95% CI: 0.82, 0.94 for low birthweight, and 0.88; 95% CI: 0.84, 0.93 for preterm birth, respectively]. CONCLUSION: Latin American immigrant women had better birth outcomes in Canada than in Spain, suggesting a more selective migration in Canada than in Spain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".