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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".