Instructor Development Workshops for Advanced Life Support Training Courses Held in a Fully Virtual Space: Observational Study
Bibliographic record
Abstract
BACKGROUND: Various face-to-face training opportunities have been lost due to the COVID-19 pandemic. Instructor development workshops for advanced resuscitation (ie, advanced life support) training courses are no exception. Virtual reality (VR) is an attractive strategy for remote training. However, to our knowledge, there are no reports of resuscitation instructor training programs being held in a virtual space. OBJECTIVE: This study aimed to investigate the learning effects of an instructor development workshop that was conducted in a virtual space. METHODS: In this observational study, we created a virtual workshop space by using NEUTRANS (Synamon Inc)-a commercial VR collaboration service. The instructor development workshop for the advanced life support training course was held in a virtual space (ie, termed the VR course) as a certified workshop by the Japanese Association of Acute Medicine. We asked 13 instructor candidates (students) who participated in the VR course to provide a workshop report (VR group). Reports from a previously held face-to-face workshop (ie, the face-to-face course and group) were likewise prepared for comparison. A total of 5 certified instructor trainers viewed and scored the reports on a 5-point Likert scale. RESULTS: All students completed the VR course without any problems and received certificates of completion. The scores for the VR group and the face-to-face group did not differ at the level of statistical significance (median 3.8, IQR 3.8-4.0 and median 4.2, IQR 3.9-4.2, respectively; P=.41). CONCLUSIONS: We successfully conducted an instructor development workshop in a virtual space. The degree of learning in the virtual workshop was the same as that in the face-to-face workshop.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".