A Systematic Review of Racial and Ethnic Disparities in Maternal Health Outcomes among Asians/Pacific Islanders
Bibliographic record
Abstract
Efforts to improve women's health and to reduce maternal mortality worldwide have led to a notable reduction in the global maternal mortality ratio (MMR) over the past two decades. However, it is clear that maternal health outcomes are not equitable, especially when analyzing the scope of maternal health disparities across "developed" and "underdeveloped" nations. This study evaluates recent MMR scholarship with a particular focus on the racial and ethnic divisions that impact on maternal health outcomes. The study contributes to MMR research by analyzing the racial and ethnic disparities that exist in the US, especially among Asian and Pacific Islander (API) subgroups. The study applies exclusionary criteria to 710 articles and subsequently identified various maternal health issues that disproportionately affect API women living in the US. In applying PRISMA review guidelines, the study produced 22 peer-reviewed articles that met inclusionary and exclusionary criteria for this review. The data analysis identified several maternal health foci: obstetric outcomes, environmental exposure, obstetric care and quality measures, and pregnancy-related measures. Only eight of the 22 reviewed studies disaggregated API populations by focusing on specific subgroups of APIs, which signals a need to reconceptualize marginalized API communities' inclusion in health care systems, to promote their equitable access to care, and to dissolve health disparities among racial and ethnic divides. Several short- and long-term initiatives are recommended to develop and implement targeted health interventions for API groups, and thus provide the groundwork for future empirically driven research among specific API subgroups in the US.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".