Understanding Historical Trauma Among Urban Indigenous Adults at Risk for Diabetes
Bibliographic record
Abstract
Historical trauma has been posited as a key framework for conceptualizing and addressing health equity in Indigenous populations. Using a community-based participatory approach, this study aimed to examine historical trauma and key psycho-social correlates among urban Indigenous adults at risk for diabetes to inform diabetes and other chronic disease prevention strategies. Indigenous adult participants (n=207) were recruited from an urban area in California and were asked to identify whether their Indigenous heritage was from a group in the United States, Canada, or Latin America. Historical trauma was assessed using the Historical Loss (HLS) and Historical Loss Associated Symptoms (HLAS) scales. Nearly half (49%) of Indigenous participants from the United States or Canada endorsed thinking about one or more historical losses weekly, daily, or several times a day, compared to 32% for Indigenous participants from Mexico, Central America, and South America. Most participants (62%) reported experiencing one or more historical loss-associated symptoms, such as depression and anger, sometimes, often, or always. Ancestry from the United States or Canada, depression, and participation in cultural activities were associated with greater HLS and HLAS scores, indicating a greater number of losses and associated symptoms. Results suggest a need to consider historical trauma when designing diabetes prevention interventions and the need to further consider ancestry differences. As preventive efforts for Indigenous adults expand in urban environments, behavioral interventions must incorporate strategies that address community-identified barriers in order to succeed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".