Maternal Mortality Among American Indian/Alaska Native Women: A Scoping Review
Bibliographic record
Abstract
Background: Maternal mortality decreased globally by about 38% between 2000 and 2017, yet, it continues to climb in the United States. Gaping disparities exist in U.S. maternal mortality between white (referent group) and minority women. Despite important and appropriate attention to disparities for black women, almost no attention has been given to American Indian/Alaska Native (AI/AN) women. The purpose of this scoping review is to synthesize available literature concerning AI/AN maternal mortality. Methods: Databases were searched using the terms maternal mortality and pregnancy-related death, each paired with American Indian, Native American, Alaska Native, Inuit, and Indigenous. Criteria ( e.g ., hemorrhage) were paired with initial search terms. Next, pregnancy-associated death was paired with American Indian, Native American, Alaska Native, Inuit, and Indigenous. Criteria in this category were homicide, suicide, and substance use. Results: The three leading causes of AI/AN pregnancy-related maternal mortality are hemorrhage, cardiomyopathies, and hypertensive disorders of pregnancy. AI/AN maternal mortality data for homicide and suicide consistently include small samples and often categorize AI/AN maternal deaths in an “Other” race/ethnicity, which precludes targeted AI/AN data analysis. No studies that reported AI/AN maternal mortality as a result of substance use were found. Health care characteristics such as quality, access, and location also may influence maternal outcomes and maternal mortality. Conclusions: Despite AI/AN maternal mortality being disproportionately high compared to other racial/ethnic groups, relatively little is known about root causes.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".