Ground-up approach to understanding the impacts of historical trauma in one reserve-dwelling first nations community.
Bibliographic record
Abstract
OBJECTIVE: First Nations peoples experience disproportionate health inequities compared to most non-Indigenous populations. Historical trauma is one factor that has received growing attention in relation to health inequities among First Nations populations. The goal of the present study was to improve understanding of the specific forms, impacts, and mechanisms of transmission of events that lead to historical trauma and the historical trauma response in First Nations peoples. METHOD: = 34; 70.4% female). RESULTS: Conventional content analysis revealed the numerous forms that historical trauma take in this First Nations community; individual-, familial-, community-, and societal-level impacts of historical trauma; and ways in which historical trauma has been transmitted in this community. Loss of culture, alcohol use, and parenting were major themes identified across these domains. CONCLUSIONS: Findings provide important information on the experience of historical trauma in one First Nations community, highlighting the roles of loss of culture; alcohol use; and parenting in the forms, impacts, and transmission of historical trauma. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".