A Model for Identifying and Ranking Need for Trauma Service in Nonmetropolitan Regions Based on Injury Risk and Access to Services
Bibliographic record
Abstract
BACKGROUND: Timely access to definitive trauma care has been shown to improve survival rates after severe injury. Unfortunately, despite development of sophisticated trauma systems, prompt, definitive trauma care remains unavailable to over 50 million North Americans, particularly in rural areas. Measures to quantify social and geographic isolation may provide important insights for the development of health policy aimed at reducing the burden of injury and improving access to trauma care in presently under serviced populations. METHODS: Indices of social deprivation based on census data, and spatial analyses of access to trauma centers based on street network files were combined into a single index, the Population Isolation Vulnerability Amplifier (PIVA) to characterize vulnerability to trauma in socioeconomically and geographically diverse rural and urban communities across British Columbia. Regions with a sufficient core population that are more than one hour travel time from existing services were ranked based on their level of socioeconomic vulnerability. RESULTS: Ten regions throughout the province were identified as most in need of trauma services based on population, isolation and vulnerability. Likewise, 10 communities were classified as some of the least isolated areas and were simultaneously classified as least vulnerable populations in province. The model was verified using trauma services utilization data from the British Columbia Trauma Registry. These data indicate that including vulnerability in the model provided superior results to running the model based only on population and road travel time. CONCLUSIONS: Using the PIVA model we have shown that across Census Urban Areas there are wide variations in population dependence on and distances to accredited tertiary/district trauma centers throughout British Columbia. Many of the factors that influence access to definitive trauma care can be combined into a single quantifiable model that researchers in the health sector can use to predict where to place new services. The model can also be used to locate optimal locations for any basket of health services.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".