Disparities in Access to Trauma Care in Canada: A Geospatial Analysis of Census Data
Bibliographic record
Abstract
Objective: To assess the proportion of the Canadian population residing within 1 hour of definitive trauma care at Level 1 and 2 trauma centers, and to examine the sociodemographic characteristics of individuals living beyond this range. Background: Disparities in access to trauma care remain a significant challenge in Canada, particularly for individuals in rural and remote communities. These inequities, often influenced by geographic isolation, limited resources, and systemic barriers, adversely impact patient outcomes. Methods: Geographic Information System methods were employed to define 1-hour ground and air transport catchment areas for adult Level 1 and 2 trauma centers across Canada. The study utilized Statistics Canada Census 2021 data to calculate the population (aged ≥ 15 years) living within and outside the 1-hour distance of these centers. Results: The majority of the adult Canadian population (75.8%; 23,475,747) lives within 1 hour of 32 designated Level 1 and Level 2 trauma centers. Conversely, the population living outside this range (24.2%; 7,503,439) is more likely to be unemployed (12.0% vs 8.0%, P < 0.05), without postsecondary education (21.2% vs 13.6%, P < 0.05), with household incomes of less than 60,000 $/year (10.9% vs 1.7%, P < 0.05), and of Indigenous origin (13.1% vs 3.2%, P < 0.05). With helicopter transport, the population within 1 hour increases to 90.3% (27,981,510); however, socioeconomic disparities persist for populations outside the 1-hour range. Conclusions: Disparities in access to definitive trauma care persist across Canada, disproportionately affecting lower socioeconomic and Indigenous populations, even with helicopter transport. Targeted efforts are needed to enhance trauma care delivery to these underserved groups.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".