Management of Cold Water-induced Hypothermia: A Simulation Scenario for Layperson Training Delivered via a Mobile Tele-simulation Unit
Bibliographic record
Abstract
Newfoundland and Labrador (NL) has one of the highest provincial drowning rates in Canada, largely due to the many rural communities located near bodies of water. Factor in the province's cold climate (average NL's freshwater temperature is below 5.4°C)and the prevalence of winter recreational activities among the population, there exists an inherent risk of ice-related injuries and subsequent hypothermia. Oftentimes, these injuries occur in remote/rural settings where immediate support from Emergency Medical Services (EMS) may not be available. During this critical period, it frequently falls on individuals without formal healthcare training to provide lifesaving measures until help arrives. Training individuals in rural communities plays an important role in ensuring public safety. In recent years, simulation-based education has become an essential tool in medical, marine and first aid training. It provides learners with a safe environment to hone their skills and has been shown to be superior to traditional clinical teaching methods. The following case aims to train laypeople from rural settings in the immediate management of an individual who becomes hypothermic following immersion into cold water. However, reaching these individuals to provide training can be a challenge in a province with such a vast geography. To assist with overcoming this, the development of a simulation center that is portable between communities (or Mobile Tele-Simulation Unit) has occurred. By utilizing modern technology, this paper also proposes an innovative method of connecting with learners in more difficult to reach regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".