V-Mart, a Virtual Reality Grocery Store
Bibliographic record
Abstract
OBJECTIVE: This study examined the potential usability, relevance, and acceptability of V-Mart, a virtual reality grocery store as an assessment and intervention tool for veterans with mild traumatic brain injury. DESIGN: Six focus groups were conducted for a 2-yr period to assess perceptions from the following three key stakeholders: therapists, veterans with neither mild traumatic brain injury nor posttraumatic stress disorder, and veterans with mild traumatic brain injury with or without posttraumatic stress disorder (mild traumatic brain injury/posttraumatic stress disorder). The System Usability Scale was applied as an objective measure of usability. Transcripts from the six focus groups were subjected to thematic analyses using the constant, comparative method. RESULTS: The focus groups indicated that V-Mart was perceived as highly usable, relevant, and acceptable. Early technical problems were resolved satisfactorily. Therapists indicated that they would use an application such as V-Mart if it were available. The veterans with neither mild traumatic brain injury nor posttraumatic stress disorder felt that it was realistic and likely to be useful, as did the veterans with mild traumatic brain injury/posttraumatic stress disorder. The System Usability Scale mean follow-up scores ranged from 71.4 to 86.0, surpassing the threshold for acceptable usability in health care settings. CONCLUSIONS: Focus group and System Usability Scale data indicate that the V-Mart has great potential as an assessment tool and intervention for veterans with mild traumatic brain injury/posttraumatic stress disorder. Further development and clinical trials are warranted.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".