Migrant Women's Utilization of Prenatal Care: A Systematic Review
Bibliographic record
Abstract
Our objectives were to determine whether migrant women in Western industrialized countries have higher odds of inadequate prenatal care (PNC) compared to receiving-country women and to summarize factors that are associated with inadequate PNC among migrant women in these countries. We conducted searches of electronic databases (MEDLINE, EMBASE, and PsycINFO), reference lists, known experts, and an existing database of the Reproductive Outcomes And Migration international research collaboration for articles published between January, 1995 and April, 2010. Title and abstract review and quality appraisal were conducted independently by 2 reviewers using established criteria, with consensus achieved through discussion. In this systematic review of 29 studies, the majority of studies demonstrated that migrant women were more likely to receive inadequate PNC than receiving-country women, with most reporting moderate to large effect sizes. Rates of inadequate PNC among migrant women varied widely by country of birth. Only three studies explored predictors of inadequate PNC among migrant women. These studies found that inadequate PNC among migrant women was associated with being less than 20 years of age, multiparous, single, having poor or fair language proficiency, education less than 5 years, an unplanned pregnancy, and not having health insurance. We concluded that migrant women as a whole were more likely to have inadequate PNC and the magnitude of this risk differed by country of origin. Few studies addressed predictors of PNC utilization in migrant women and this limits our ability to provide effective PNC in this population.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".