A Meta-Analysis of the Correlation Between Historical Trauma and Health Outcomes in the Native American Population
Bibliographic record
Abstract
Native Americans experience significant health disparities such as increased rates ofcardiovascular disease, diabetes, and mental illness. Recent research has suggested that historical trauma may be a contributing factor. This meta-analysis examined the association between historical trauma and health outcomes in Native Americans in the United States and Canada. Data from 14 studies (N = 14,698, 35 effect sizes) examining the physical health, mental health, and substance use domains and using the Historical Loss Scale were collected for analysis. Possible moderating factors were also examined. Overall, a small, significant association (r =.124) was found between historical trauma and health outcomes. The association was significantfor mental health outcomes (r = .181), but not physical health (r = .169) or substance use (r =.038). Effect sizes were not moderated by age group, gender, or reservation residency. Findings largely support the theory of historical trauma as a contributor to health inequities. Future research is necessary, and should be expanded to further test the Historical Loss Scale, collect more health outcome data, and survey Native Americans across the United States.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".