A Feasibility Test of a Brief Mobile Virtual Reality Meditation for Frontline Healthcare Workers in a Hospital Setting
Bibliographic record
Abstract
The purpose of this study was to examine whether a virtual reality plus neurofeedback (VR+NF) meditation experience (experimental condition) was more effective than a standard guided audio-only meditation (control condition) in improving mood in one hundred healthcare workers. Data collection occurred in a hospital setting between October, 2020 and March, 2021 at the height of the COVID-19 pandemic. Participants were alternately assigned to one of the two conditions. Before and after the meditation experience, participants completed the Brunel Mood Scale. Results indicated that both groups showed a similar and significant decrease in Anger, Tension, and Depression. On scales measuring Vigor, Fatigue, and Confusion, the VR+NF group showed decreases, while the audio-only group showed no significant change. The VR+NF group showed significant increases on the Calmness and Happiness scales, which did not change significantly in the audio-only group. These results suggest that the addition of VR and neurofeedback may increase the positive outcomes associated with standard audio-guided meditation. These increased benefits may be due to the sense of presence intrinsic to VR, the inclusion of nature-based scenes in the VR experience, as well as the increased self-awareness created by the addition of neurofeedback. As the pre and post measures take place within one 50-min session, further studies assessing the longer-term changes are needed.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".