Mobile medical simulation for rural anesthesia providers: A feasibility study
Bibliographic record
Abstract
INTRODUCTION: Family practice anesthesia (FPA) providers are family physicians trained to deliver anesthesia care; they often practice in rural hospitals to facilitate surgical care. FPA providers in rural hospitals face challenges including professional isolation and limited opportunities for formal continuing education. To address needs identified by FPA providers, we piloted mobile medical simulation in rural Saskatchewan. METHODS: Using a logic model framework, we evaluated feasibility of a one-day interdisciplinary mobile simulation workshop for healthcare providers in a rural Saskatchewan hospital. As part of this mixed methods pilot study, we interviewed stakeholders to explore their perceptions of human and financial resources associated with delivering medical simulations in rural locations. Multiple simulation scenarios were utilized to train participants in clinical and professional skills. Participants completed pre- and post-workshop surveys to evaluate their experience. RESULTS: = 10) reported improved knowledge and found the experience valuable. The session prompted participants to reflect on their deficiencies in certain clinical procedures/skills and highlight learning strategies to address the gap. DISCUSSION: Mobile medical simulation brought continuing medical education (CME) to health professionals in a rural location, but the program was expensive. Our logic model may inform educators and administrators considering mobile medical simulation for physicians in rural areas when balancing resource allocation and the organization's commitment to CME for rural physicians.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".