Bringing Trauma Education to the Frontier: Overcoming Distance Barriers Utilizing a Virtual Platform
Bibliographic record
Abstract
BACKGROUND: The American College of Surgeons and state regulations mandate that trauma facilities offer trauma-specific continuing education throughout the region they serve. These requirements come with unique challenges when serving a rural and sparsely populated state. A novel approach to providing education was necessitated by the coronavirus disease 2019 pandemic, travel distance, and limited local specialists. OBJECTIVE: The purpose of this article is to describe the development of a virtual educational program used to improve access to quality trauma education and decrease barriers to obtaining continuing education hours inherent in the region. METHODS: This article describes the development and implementation of the Virtual Trauma Education program, which provided one free continuing education hour per month from October 2020 to October 2021. The program reached more than 2,000 viewers and established a method to provide continuous monthly educational offerings throughout the region. RESULTS: After the Virtual Trauma Education program implementation, monthly educational attendance increased from an average of 55 to 190. Viewership data indicate that trauma education across our region is far more robust, available, and accessible using a virtual platform. With more than 2,000 views from October 2020 to October 2021, Virtual Trauma Education offerings have spread far beyond regional borders, reaching 25 states and 169 communities. CONCLUSION: Virtual Trauma Education delivers easily accessible trauma education and is a program that has proven its sustainability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".