Opportunities for Managing Pain and Anxiety in the Intensive Care Unit Using Virtual Reality: Perspectives from Bedside Care Providers
Bibliographic record
Abstract
Purpose: Virtual reality (VR) is a promising nonpharmacological tool to decrease pain and anxiety in the intensive care unit (ICU). Limited work, however, has been conducted on the functional implications of using VR to support intubated ICU patients. This study aimed to gather ICU providers' perspectives on implementing VR in the ICU to understand potential challenges and establish measurable outcomes. Materials and Methods: This was an exploratory qualitative study. ICU bedside care providers were shown how VR technology would look on an intubated mannequin, had the opportunity to experience VR, and took part in a semistructured interview. Results: = 16) expressed an overall willingness to use VR in the ICU with intubated patients, indicating that VR may be particularly beneficial for prolonged intubation, but less optimal during extubation. Potential challenges included patient eligibility for use, informed consent, provider buy-in and workflow, and appropriateness of VR hardware/software. Conclusions: Successful adoption of VR in the ICU is contingent on provider involvement in designing staff training, best practices, and standard workflow for administering and evaluating the intervention. Although many concerns described by providers in our sample have potential solutions, staff preparation and family involvement remain critical to reducing the complexity of staff workflow for administering this intervention.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".