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 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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".