The influence of refugee status and secondary migration on preterm birth
Bibliographic record
Abstract
BACKGROUND: It is unknown whether the risk of preterm birth (PTB) is elevated for forced (refugee) international migrants and whether prolonged displacement amplifies risk. While voluntary migrants who arrive from a country other than their country of birth (ie, secondary migrants) have favourable birth outcomes compared with those who migrated directly from their country of birth (ie, primary migrants), secondary migration may be detrimental for refugees who experience distinct challenges in transition countries. Our objectives were (1) to determine whether refugee status was associated with PTB and (2) whether the relation between refugee status and PTB differed between secondary and primary migrants. METHODS: We conducted a retrospective population-based cohort study. Ontario immigration (2002-2010) and hospitalisation data (2002-2010) were linked to estimate adjusted cumulative odds ratios (ACOR) of PTB (22-31, 32-36, 37-41 weeks of gestation), with 95% CIs (95% CI) comparing refugees with non-refugees. We further included a product term between refugee status and secondary migration. RESULTS: Overall, refugees (N=12 913) had 17% greater cumulative odds of short gestation (ACOR=1.17, 95% CI 1.07 to 1.28) compared with non-refugees (N=110 640). Secondary migration modified the association between refugee status and PTB (p=0.007). Secondary refugees had 58% greater cumulative odds of short gestation (ACOR=1.58, 95% CI 1.25 to 2.00) than secondary non-refugees, while primary refugees had 12% greater cumulative odds of short gestation (ACOR=1.12, 95% CI 1.02 to 1.23) than primary non-refugee immigrants. CONCLUSIONS: Refugee status, jointly with secondary migration, influences PTB among migrants.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".