The Missing and Murdered Indigenous Relatives Crisis and the Life Expectancy Gap for Native Americans, 2010–2019
Bibliographic record
Abstract
BACKGROUND: We assessed the role of missing and murdered indigenous relatives (MMIR) relevant causes of death in the life expectancy gap between the American Indian and Alaska Native (AIAN) and non-Hispanic White populations. METHODS: Using 2010-2019 National Center for Health Statistics Detailed Mortality files, we created multidecrement life tables and used the age-incidence decomposition method to identify (1) the causes of death that contribute to the gap in life expectancy between White and AIAN, and (2) the mechanisms through which these causes operate. RESULTS: Causes of death relevant to MMIR constituted 4.0% of all AIAN deaths, but accounted for almost one-tenth (9.6%; 0.74 of 8.21 years) of the overall AIAN-White life expectancy gap. MMIR-relevant causes accounted for 6.6% of the AIAN-White life expectancy gap for women and 11.9% of the for men. CONCLUSIONS: This study suggests a critical agenda for research on racial inequities in mortality, with a focus on MMIR.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".