International migration and gestational diabetes mellitus: a systematic review of the literature and meta‐analysis
Bibliographic record
Abstract
Influxes of migrant women of childbearing age to receiving countries have made their perinatal health status a key priority for many governments. The international research collaboration Reproductive Outcomes And Migration (ROAM) reviewed published studies to assess whether migrants in countries of resettlement have a greater risk of gestational diabetes mellitus (GDM) than women in receiving countries. A systematic review of the literature from Medline, Embase, PsychInfo and CINAHL from 1990 to 2009 included studies of migrant women and GDM. Studies were excluded if there was no cross-border movement or comparison group or if the receiving country was not the country of resettlement. Studies were assessed for quality, analysed descriptively and meta-analysed. Twenty-four reports (representing >120,000 migrants) met our inclusion criteria. Migrants were described primarily by geographic origin; other relevant aspects (e.g. time in country, language fluency) were rarely studied. Migrants' results for GDM were worse than those for receiving-country women in 79% of all studies. Meta-analyses showed that, compared with receiving-country women, Caribbean, African, European and Northern European women were at greater risk of GDM, while North Africans and North Americans had risks similar to receiving-country women. Although results of the 31 comparisons of Asians, East Africans or non-Australian Oceanians were too heterogeneous to provide a single GDM risk estimate for migrant women, only one comparison was below the receiving-country comparison group, all others presented a higher risk estimate. The majority of women migrants to resettlement countries are at greater risk for GDM than women resident in receiving countries. Research using clear, specific migrant definitions, adjusting for relevant risk factors and including other aspects of migration experiences is needed to confirm and understand these findings.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".