Telesimulation in healthcare education: A scoping review
Bibliographic record
Abstract
OBJECTIVES: To provide a comprehensive overview on the utilization and effectiveness of telesimulation in healthcare education. DESIGN: A scoping review. DATA SOURCES: A search of five databases including PubMed, Web of Science, Cochrane, EMBASE and ProQuest was conducted between 2000 and 2022. REVIEW METHODS: Arksey and O' Malley's scoping review framework was utilised. Data were narratively synthesised. RESULTS: 29 articles were included. More than half of the publications on telesimulation were borne out of need during the COVID-19 pandemic. Innovation reports were the most prevalent publications followed by descriptive studies. Telesimulation was applied for the delivery of diverse learning content including patient care management, procedural skills and team training. A variety of videoconferencing software and simulation modalities have been used for telesimulation. Telesimulation was generally well-received, despite its technical challenges. Learning effectiveness of telesimulation was evident in quasi-experimental studies. CONCLUSION: Telesimulation has been gaining acceptance as a distance-based simulation education modality. It will continue to evolve and potentially blend with in-person simulation. More rigorous research is warranted to evaluate learning outcomes and establish best practices in telesimulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".