Rural trauma telementoring: a pilot project
Bibliographic record
Abstract
BACKGROUND: Given Canada's geographically dispersed population, initial trauma care may occur at rural sites that may not manage patients with trauma frequently; thus, telementoring can play a life-saving role. In this article, we describe a rural trauma telementoring pilot program in British Columbia and report the results of an evaluation of its strengths and weaknesses. METHODS: Trauma surgeons from a quaternary trauma centre in Vancouver helped facilitate 3 in situ trauma simulation sessions at a rural BC hospital between fall 2019 and summer 2020. The sessions involved 4 physician participants (a trauma surgeon telementor, a family physician with additional expertise in emergency medicine acting as trauma team leader, a family physician with additional expertise in anesthesia and a family physician with Enhanced Surgical Skills), an emergency department nurse, 2 operating room/trauma team nurses, and laboratory and radiology technicians. The sessions involved simulated damage-control procedures and lasted about 2 hours. The participants completed surveys assessing comfort and confidence regarding aspects of trauma care and use of the telehealth unit before and after each session, and the facilitators assessed team dynamics using the Modified Non-Technical Skills for Trauma (T-NOTECHS) tool. Focus groups were held to gather qualitative data, and costs were tracked. RESULTS: = 0.02). Qualitative analysis identified 3 dominant themes: telementoring increased provider confidence, telementoring increased order to the resuscitation procedure and the technical aspects of telementorship. The telementoring program was well received by all participants. CONCLUSION: A significant improvement was seen across simulations in physician confidence and trauma team dynamics with telementorship support. Telementoring in trauma may provide a way to lessen the difference between rural and urban patient outcomes within Canada's geographically dispersed population, although further work investigating the impact of its use in real-life patients, as well as barriers to its implementation, is required.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".