Consistent Honor, Persistent Disadvantage: American Indian and Alaska Native Veteran Health in the National Survey of Veterans
Bibliographic record
Abstract
Objective: To examine self-rated health and activities of daily living (ADLs) limitations among American Indian and Alaska Native (AI/AN) veterans relative to white veterans. Methods: We use the 2010 National Survey of Veterans and limit the sample to veterans who identify as AI/AN or non-Hispanic white. We calculated descriptive statistics, confidence intervals, and used logistic regression. Results: AI/AN veterans are younger, have lower levels of income, and have higher levels of exposure to combat and environmental hazards compared to white veterans. We found that AI/AN veterans are significantly more likely to report fair/poor health controlling for socioeconomic status and experience an ADL controlling for age, health behaviors, socioeconomic status, and military factors. Discussion: The results indicate that AI/AN veterans are a disadvantaged population in terms of their health and disability compared to white veterans. AI/AN veterans may require additional support from family members and/or Veteran Affairs to address ADLs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".