The state of distance healthcare simulation during the COVID-19 pandemic: results of an international survey
Bibliographic record
Abstract
BACKGROUND: The coronavirus pandemic continues to shake the embedded structures of traditional in-person education across all learning levels and across the globe. In healthcare simulation, the pandemic tested the innovative and technological capabilities of simulation programs, educators, operations staff, and administration. This study aimed to answer the question: What is the state of distance simulation practice in 2021? METHODS: This was an IRB-approved, 34-item open survey for any profession involved in healthcare simulation disseminated widely and internationally in seven languages from January 14, 2021, to March 3, 2021. Development followed a multistep process of expert design, testing, piloting, translation, and recruitment. The survey asked questions to understand: Who was using distance simulation? What driving factors motivated programs to initiate distance sim? For what purposes was distance sim being used? What specific types or modalities of distance simulation were occurring? How was it being used (i.e., modalities, blending of technology and resources and location)? How did the early part of the pandemic differ from the latter half of 2020 and early 2021? What information would best support future distance simulation education? Data were cleaned, compiled, and analyzed for dichotomized responses, reporting frequencies, proportions, as well as a comparison of response proportions. RESULTS: From 32 countries, 618 respondents were included in the analysis. The findings included insights into the prevalence of distance simulation before, during, and after the pandemic; drivers for using distance simulation; methods and modalities of distance simulation; and staff training. The majority of respondents (70%) reported that their simulation center was conducting distance simulation. Significantly more respondents indicated long-term plans for maintaining a hybrid format (82%), relative to going back to in-person simulation (11%, p < 0.001). CONCLUSION: This study gives a perspective into the rapid adaptation of the healthcare simulation community towards distance teaching and learning in reaction to a radical and quick change in education conditions and environment caused by COVID-19, as well as future directions to pursue understanding and support of distance simulation.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".