Access to Trauma Systems in Canada
Bibliographic record
Abstract
BACKGROUND: Trauma is a leading cause of morbidity, potential years of life lost and health care expenditure in Canada and around the world. Trauma systems have been established across North America to provide comprehensive injury care and to lead injury control efforts. We sought to describe the current status of trauma systems in Canada and Canadians' access to acute, multidisciplinary trauma care. METHODS: A national survey was used to identify the locations and capabilities of adult trauma centers across Canada and to identify the catchment populations they serve. Geographic information science methods were used to map the locations of Level I and Level II trauma centers and to define 1-hour road travel times around each trauma center. Data from the 2006 Canadian Census were used to estimate populations within and outside 1-hour access to definitive trauma care. RESULTS: In Canada, 32 Level I and Level II trauma centers provide definitive trauma care and coordinate the efforts of their surrounding trauma systems. Most Canadians (77.5%) reside within 1-hour road travel catchments of Level I or Level II centers. However, marked geographic disparities in access persist. Of the 22.5% of Canadians who live more than an hour away from a Level I or Level II trauma centers, all are in rural and remote regions. DISCUSSION: Access to high quality acute trauma care is well established across parts of Canada but a clear urban/rural divide persists. Regional efforts to improve short- and long-term outcomes after severe trauma should focus on the optimization of access to pre-hospital care and acute trauma care in rural communities using locally relevant strategies or novel care delivery options.
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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 | 0.009 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".