The impact of historical trauma on health outcomes for indigenous populations in the USA and Canada: A systematic review.
Bibliographic record
Abstract
Beginning in the mid-1990s, the construct of historical trauma was introduced into the clinical and health science literatures to contextualize, describe, and explain disproportionately high rates of psychological distress and health disparities among Indigenous populations. As a conceptual precursor to racial trauma, Indigenous historical trauma (IHT) is distinguished by its emphasis on ancestral adversity that is intergenerationally transmitted in ways that compromise descendent well-being. In this systematic review of the health impacts of IHT, 32 empirical articles were identified that statistically analyzed the relationship between a measure of IHT and a health outcome for Indigenous samples from the United States and Canada. These articles were categorized based on their specific method for operationalizing IHT, yielding 19 articles that were grouped as historical loss studies, 11 articles that were grouped as residential school ancestry studies, and three articles that were grouped as "other" studies. Articles in all three categories included diverse respondents, disparate designs, varied statistical techniques, and a range of health outcomes. Most reported statistically significant associations between higher indicators of IHT and adverse health outcomes. Analyses were so complex, and findings were so specific, that this groundbreaking literature has yet to cohere into a body of knowledge with clear implications for health policy or professional practice. At the conceptual level, it remains unclear whether IHT is best appreciated for its metaphorical or literal functions. Nevertheless, the enthusiasm surrounding IHT as an explanation for contemporary Indigenous health problems renders it imperative to refine the construct to enable more valid research. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".