Concurrent Validity of a Virtual Reality Driving Assessment for Persons with Brain Injury
Bibliographic record
Abstract
In this article, we present the results of a pilot study to examine the driving performance of persons with brain injury using virtual reality (VR) technology. A total of 28 adult persons with a brain injury (22 males, 6 females) participated in a standardized driving evaluation, which included a VR driving environment, known as the DriVR. Concurrent validity of the DriVR was examined by comparing DriVR measures to other indicators of driving ability, which consisted of on-road, cognitive and visual-perceptual, and driving video tests. Statistically significant DriVR inter-correlations using the Bonferroni correction were found between following a pace car (Follow Traffic Event), and correctly parking a car (Driveway Choice Event) (r pb = - .65, p< .003), as well as for two measures of lane tracking (Shop Road and Opposite Road), (r = .98, p< .003). The DriVR appeared to be a useful adjunctive screening tool for assessing driving performance in persons with brain injury. However, as with any new assessment and intervention tool, it will need to undergo further empirical validation.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".