P.188 Neurotrauma in Indigenous populations of Canada: challenges and future directions: A Scoping Review
Bibliographic record
Abstract
Background: Neurotrauma accounts for over 24 000 hospitalizations annually in Canada. Among those affected, Indigenous peoples are disproportionately impacted. The goal of this scoping review is to identify factors underlying these disparities. Methods: A scoping review was conducted to collect papers pertaining to neurotrauma in Indigenous populations of Canada. Using MEDLINE, 676 articles were screened with MeSH terms including ‘Indigenous’, ‘spinal cord injuries’, ‘brain injuries, traumatic’ and ‘Canada’ as of April 2021. Results: Studies report over twice the incidence of traumatic brain injury and traumatic spinal cord injury in Indigenous populations compared to non-Indigenous populations. The burden of neurotrauma is attributable to infrastructure disparities in rural communities and reserves, elevated rates of substance use and violence, and inequities in treatment and rehabilitation following injury. These issues are deeply rooted in the trauma endured by Indigenous peoples through the course of Canadian history, owing to government policies that severely impacted their socioeconomic conditions, culture, and access to healthcare services. Conclusions: Systems-level interventions guided by Indigenous community members will help to address the disparities that Indigenous peoples face in the care and rehabilitation of neurotrauma. This study will inform further research of culturally appropriate approaches to reduce neurotrauma burden among Indigenous peoples.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".