The impact of immigrant-specific policies and programmes on maternal, perinatal and paediatric outcomes: a scoping review
Bibliographic record
Abstract
Introduction: We aimed to synthesise the current literature to better understand the scope and impacts of immigration policies and programmes on the health and development of immigrant mothers and their children. Methods: Six databases (Scopus, Embase, Medline, Global Health, CINAHL and Public Health Database) were systematically searched from inception to October 2022 using keywords and subject headings for immigration, policy/programme and maternal/perinatal/paediatric outcomes. We analysed policies by the services or processes they affected, whether they restricted or supported immigrants' access or eligibility, and by maternal/perinatal or paediatric outcomes, using a narrative synthesis. Results: Out of 10 105 identified studies, 65 met our inclusion criteria. Eligible studies were published from 2000 onwards and evaluated outcomes over periods that ranged from 1980 to 2019. Most studies focused on immigrants in the USA (n=54, 83.1%), and many employed quasi-experimental methods (n=42, 64.6%). Commonly evaluated policies or programmes were related to health insurance and access to other public benefits (n=22, 33.8%), followed by those related to naturalisation or legalisation (n=8, 12.3%), and the selection, reception or integration of immigrants (n=8, 12.3%). Overall, paediatric outcomes (n=40, 61.5%) were examined more than maternal or perinatal outcomes (n=29, 44.6%). Few studies explored postpartum care (n=2, 3.1%), childhood vaccination (n=1, 1.5%) or mental health outcomes (n=5, 7.7%). Restrictive immigration policies were not associated with birth outcomes but negatively impacted prenatal care and healthcare utilisation among vulnerable subgroups of immigrant women. Supportive policies were associated with improvements in prenatal care among immigrant mothers, an increase in health insurance coverage among children, and positive impacts on child educational outcomes. Conclusions: Research on the impacts of immigrant-specific policies and programmes on maternal and child outcomes remains limited, primarily focusing on the immigrant receiving context in the USA. More research from other countries is needed to better understand policy impacts on a global scale. Future studies should consider a broader range of indicators that include postpartum outcomes, childhood vaccination and mental health.
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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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".