Disparities in Access to Trauma Care in Canada: A Geospatial Analysis of Census Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it