Assessing Patient Acceptance of Being Offered a Virtual Calm Room in Psychiatric Inpatient Care: Mixed Methods Study
Bibliographic record
Abstract
Background: Implementing new technologies in health care settings is often a complex and challenging process. Virtual reality (VR) has demonstrated promising results in terms of feasibility, acceptability, and effectiveness across various health conditions. VR can provide calming environments to enhance patients' well-being in psychiatric inpatient care. However, little research has been done on patients' acceptance of VR technology in psychiatric care in the real-world setting. Objective: This study aimed to explore patients' experiences of being offered the use of a virtual calm room when feeling anxious or worried in a psychiatric inpatient setting. Methods: A mixed methods design was used, with a qualitative to quantitative approach. The data were gathered through individual interviews (n=10) and a 3-item rating scale ranging from 1 to 5 (n=59). The qualitative findings were then validated within a larger population using the quantitative data. Results: The majority of participants reported being satisfied with the option of using VR. Their initial impressions of the virtual calm room were that it seemed like a creative and stimulating environment that could potentially have a positive impact on them. They expected the VR experience to enhance their feelings of relaxation and concentration. The participants highlighted human interaction as a particularly valuable aspect to consider when implementing VR, emphasizing its role in enhancing the overall experience and ensuring a sense of connection and support throughout the process. Participants were generally positive in the answers to the survey with a high score of feeling VR to be a modern method to manage anxiety (mean 4.3, SD 0.98), low levels of experiencing difficulty (mean 2.1, SD 1.4), and would generally consider future use (mean 4.4, SD 1.1). Conclusions: The qualitative findings highlighted patients' openness to innovative methods for enhancing their engagement in the psychiatric inpatient setting. Patients expressed a desire for increased availability of the virtual calm room. However, maintaining a balance between innovative technologies and human support is crucial for the successful implementation of such methods. Quantitative results demonstrated high acceptance of the option of using the virtual calm room, with no significant difficulties reported.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".