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Record W4223439562 · doi:10.1186/s41077-022-00202-7

The state of distance healthcare simulation during the COVID-19 pandemic: results of an international survey

2022· article· en· W4223439562 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Simulation · 2022
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
FundersAmerican College of Emergency PhysiciansSociety for Academic Emergency Medicine
KeywordsDistance educationModalitiesPandemicHealth careGlobeBest practiceCoronavirus disease 2019 (COVID-19)Medical educationPsychologyComputer scienceMedicinePolitical scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.462
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it