Motor vehicle collision-related injuries and deaths among Indigenous Peoples in Canada: Meta-analysis of geo-structural factors
Bibliographic record
Abstract
Introduction: Indigenous Peoples are much more likely than non-Indigenous Peoples to be seriously injured or die in motor vehicle collisions (MVCs). This study updates and extends a previous systematic review, suggesting that future research ought to incorporate social–environmental factors.Methods: We conducted a systematic review and meta-analysis of the published and grey literature on MVCs involving Indigenous Peoples in Canada between 2010 and 2020. We focussed on personal (e.g. driving an old vehicle) and community social–environmental–economic factors (e.g. prevalent low socioeconomic status).Results: Eleven comparative cohorts that resulted in 23 at minimum, age-standardised, mortality or morbidity rate outcomes were included in our meta-analysis. Indigenous Peoples were twice as likely as non-Indigenous Peoples to be seriously injured (rate ratio [RRpooled] = 2.18) and more than 3 times as likely to die (RRpooled = 3.40) in MVCs. Such great risks to Indigenous Peoples do not seem to have diminished over the past generation. Furthermore, such risks were greater on-reserves and in smaller, rural and remote, places.Conclusion: Such places may lack community resources, including fewer transportation and healthcare infrastructural investments, resulting in poorer road conditions in Indigenous communities and longer delays to trauma care. This seems to add further evidence of geo-structural violence (geographical and institutional violence) perpetrated against Indigenous Peoples in yet more structures (i.e. institutions) of Canadian society. Canada's system of highways and roadways and its remote health-care system represent legitimate policy targets in aiming to solve this public health problem.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".