SafeVRwards: Designing a complementary virtual reality module to the Safewards framework intended to relax and manage conflict in mental health wards
Bibliographic record
Abstract
BACKGROUND: Aggression and negative activation in mental health inpatient units pose significant challenges for both patients and staff with severe physical and psychological ramifications. The Safewards model is an evidence-based conflict-containment framework including 10 strategies, such as 'Calm Down Methods'. As virtual reality (VR) scenarios have successfully enhanced anxiolytic and deactivating effects of therapeutic interventions, they are increasingly considered a means to enhance current models, like Safewards. OBJECTIVES: The present participatory design investigates the feasibility and user experience of integrating VR therapy as an add-on strategy to the Safewards model, gathering preliminary data and qualitative feedback from bedside staff in an adult inpatient mental health unit. METHODS: An exploratory within-subjects design combining qualitative observations, self-report questionnaires and semistructured interviews is employed with four nurse champions from the mental health unit at Michael Garron Hospital (Toronto, Canada). RESULTS: A chronological overview of the design process, adaptations and description of the user experience is reported. CONCLUSION: 'SafeVRwards' introduces VR as a promising conflic-containment strategy complementary to the Safewards model, which can be optimised for deployment through user-oriented refinements and enhanced customisation capacity driven by clinical staff input.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".