Mobile emergency simulation training for rural health providers
Bibliographic record
Abstract
Introduction: Mobile emergency simulation offers innovative continuing medical educational support to regions that may lack access to such opportunities.Furthermore, satisfaction is a critical element for active learning.Together, the authors evaluated Canadian rural healthcare providers' satisfaction from high fidelity emergency simulation training using a modified motorhome as a mobile education unit (MEU).Methods: Over a 5-month period, data was collected during 14 educational sessions in nine different southern Manitoban communities.Groups of up to five rural healthcare providers managed emergency simulation cases including polytrauma, severe sepsis, and inferior myocardial infarction with right ventricular involvement, followed by a debrief.Participants anonymously completed a feedback form that contained 11 questions on a five-point Likert scale and six short-answer questions.Results: Data from 131 respondents were analyzed, for a response rate of 75.6%.Respondents included nurses (27.5%), medical residents (26.7%), medical first responders (16.0%), and physicians (12.2%).The median response was 5 for overall quality of learning, development of clinical reasoning skills and decision-making ability, recognition of patient deterioration, and selfreflection.The post-simulation debrief median response was also 5 for summarizing important issues, constructive criticism, and feedback to learn.Respondents also reported that the MEU provided a believable working environment (87.0%, n=114), they had limited or no previous access to high fidelity mannequins (82.7%, n=107), and they had no specific training in crisis resource management or were unfamiliar with the term (92%, n=118).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".