Virtual reality videos for training and protocol dissemination during a pandemic
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Preparations for the COVID-19 pandemic required healthcare teams to practice known skills, such as intubation, with renewed consideration for safety, as well as develop new Standard Operating Procedures (SOPs) for health care delivery. In these conditions, translational simulation based-education (SBE) is a well-known tool that supports health care teams to improve the system using design thinking methods such as walkthroughs and team-based simulation. However, the pandemic has introduced two stressors on translational SBE simultaneously. Firstly, the need for rapid upskilling of front-line staff and rapid change to SOPs. Secondly, the need for social or physical distancing at work, such that it quickly became inappropriate for large groups of individuals to practice in-situ SBE and debrief together in close proximity. An educational approach that brings the best of translational SBE while minimizing contact and maximizing experiential learning is needed.Digital learning has been rapidly adopted by much of medical education during the pandemic. Focusing on a strong alignment between learning goals with intended clinical performance change outcomes we sought to leverage a digital education format that allowed for low barriers to adoption, yet supported the experiential, dynamic reality of translational SBE. In the absence of the ability to quickly train large numbers of people due to the need for social distancing, an immersive experience that can only be provided by virtual reality (VR) videos was the next best thing. VR, using 360-degree video, supported the creation of instructional videos from SBE events in the hospital which allow the learner to immerse and explore multiple points within the scenario. We describe how the very act of recording a video assisted in the rapid development of SOPs through translational simulation. We then describe the use of VR to stay true to the spirit of simulation for experiential learning and nearly hands-on training.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.020 |
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".