Effect of Simulation-Based Education on the Preparedness of Healthcare Professionals for the COVID-19 Pandemic: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Healthcare organizations around the world have embraced simulation to prepare healthcare professionals to the COVID-19 pandemic. However, this pandemic implies additional educational challenges in rapidly designing simulation activities, while remaining compliant with health and safety measures to prevent the spread of the virus. The effect of simulation-based education in this context remains to be evaluated. Objective: The purpose of this systematic review was to describe the features and evaluate the effect of simulation activities on the preparedness of healthcare professionals and students to safely deliver care during the COVID-19 pandemic. Methods: Databases were searched up to November 2020 using index terms and keywords related to healthcare professions, simulation, and COVID-19. All learning outcomes were considered according to the Kirkpatrick model adapted by Barr et al. (2020). Reference selection, data extraction, and quality assessment were performed in pairs and independently. Results were synthesized using meta-analytical methods and narrative summaries. Results: 22 studies were included, 21 of which were single-group studies and 14 of those included pretest/posttest assessments. Simulation activities were mostly implemented in clinical settings using manikins for training on the use of personal protective equipment, hand hygiene, identification and management of COVID-19 patients, and work processes and patient flow. Large improvements in learning outcomes after simulation activities were reported in all studies. Discussion and conclusion: Results should be interpreted cautiously due to significant threats to the internal validity of studies and the absence of control groups. However, these findings are coherent with the overall evidence on the positive effect of simulation-based education. Future studies should include control groups if feasible.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".