Telesimulation in Medical Education for High-Acuity Low-Occurrence Procedures and Clinical Encounters for Physicians and Medical Trainees in Emergency Medicine: Protocol for a Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Proficiency in high-acuity low-occurrence (HALO) procedures and clinical encounters is crucial for physicians and medical trainees in emergency medicine. Simulation-based medical education (SBME) provides valuable learning opportunities for these skills. However, accessing SBME can be challenging. Remotely delivered SBME, known as telesimulation, can enhance access to such training, especially in remote locations. OBJECTIVE Based on this review, the research team aims to evaluate the effectiveness of telesimulation in enhancing learning outcomes of HALO procedures and clinical encounters in emergency medicine. METHODS A systematic review will be conducted using the electronic databases PubMed, CINAHL, Embase, and Cochrane, focusing on studies published in English from 2011 to the present. The inclusion criteria are emergency physicians and medical trainees as learners; telesimulation sessions where the adequate performance of the procedure or clinical encounter, retention of information, or user feedback after implementing telesimulation were assessed; and original research in the form of a randomized controlled trial or nonrandomized experiments with an intervention and control group and pre- and posttest design. The exclusion criterion is defined as any study that does not fully meet the inclusion criteria. The primary outcome is the effectiveness of telesimulation in enhancing learning outcomes for HALO procedures and clinical encounters for physicians and medical trainees in emergency medicine. The secondary outcomes are the effectiveness of telesimulation for these procedures and clinical encounters delivered asynchronously and synchronously for physicians and medical trainees in emergency medicine. At least two reviewers will conduct data extraction and quality assessment. The primary and secondary outcomes will be analyzed through a systematic narrative synthesis. The methodological quality of comparative studies will be assessed using the Downs and Black checklist. The interrater reliability among the authors will be analyzed with Cohen κ. RESULTS This project was funded in the summers of 2022 and 2023 by two Summer Undergraduate Research Awards from the Memorial University of Newfoundland Faculty of Medicine. The literature search and screening will begin in April 2025, and the results of the systematic review will be available in the summer of 2026. CONCLUSIONS The results of the systematic review could inform the development of research on telesimulation for HALO procedures in emergency medicine. By investigating this topic, more effective telesimulation sessions can be designed in the future, potentially enhancing the skills of physicians and medical trainees. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/53565
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".