360-Degree Virtual Reality Video to Teach Neonatal Resuscitation: An Exploratory Development Study.
Bibliographic record
Abstract
Abstract Simulation is an effective training method for neonatal resuscitation (NR). However, the limitations brought about by the COVID-19 pandemic, and other resource constraints, have necessitated exploring alternatives. Virtual reality (VR), particularly 360-degree VR videos, have gained traction in medical training due to their immersive qualities. The primary objectives of the study were to produce a high quality 360-degree virtual reality (VR) video capturing neonatal resuscitation (NR) and to determine if it could be an acceptable adjunct to teach NR. The secondary objective was to determine which aspects of NR could benefit from the incorporation of such a video in training. This was an exploratory development study. The first part consisted of producing the video using a GoPro action camera, Adobe Premiere Pro, and Unity Editor. In the second part participants were recruited, based on level of experience, to watch the video and answer questionnaires to determine acceptability (user experience and cognitive load) and aspects of NR which could benefit from the video. The video was successfully developed. Forty-six participants showed a strong general appreciation. User experience revealed high means (> 6) in the positive subscales and low means (< 4) for immersion side effect, with no difference between groups. Cognitive load was higher than anticipated. Participants indicated that this video could be effective for teaching crisis resource management principles, human and environment interactions, and procedural skills. The 360-degree VR video could be a potential new simulation adjunct for NR. Future studies are needed to evaluate learning outcomes of such videos.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".