Sociocultural determinants of injury risk among Indigenous peoples in Canada: a population-based study of heterogeneous effects
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples in Canada experience disproportionately high injury rates compared with the general population, yet limited research has examined the sociocultural determinants underlying these disparities. Historical processes of marginalisation have resulted in contemporary disadvantages that may influence injury risk through various pathways. OBJECTIVES: This study aims to (1) examine associations between four dimensions of sociocultural determinants (income, education, residential school system and ethnic belonging) and injury risk among Indigenous populations and (2) explore the heterogeneity of these associations across demographic subgroups. METHODS: Data from the 2017 Indigenous Peoples Survey (n=20 531, weighted n=981 244) were analysed using log-binomial regression models. Average marginal effects were calculated to assess interaction effects across age groups, ethnic identities and gender. All analyses incorporated complex survey design adjustments. RESULTS: The weighted injury prevalence was 20.2% among Indigenous populations. Secondary education (RR 0.84, 95% CI [0.81, 0.87]) and ethnic belonging (RR 0.94, 95% CI [0.93, 0.95]) were associated with reduced injury risk. Income demonstrated consistent protective effects across demographic subgroups. Residential school attendance among family members significantly increased injury risk. Substantial heterogeneity was observed: education's protective effects were significant only among younger populations; ethnic belonging showed significant associations among First Nations and Inuit but not Métis; and gender-stratified analyses revealed stronger protective effects of ethnic belonging among women. CONCLUSION: Sociocultural determinants demonstrate significant but heterogeneous associations with injury risk among Indigenous populations. These findings highlight the importance of multidimensional, demographically targeted approaches to injury prevention that address both historical trauma and contemporary socioeconomic conditions, while recognising the protective capacity of ethnic belonging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".