One- and Three-Screen Driving Simulator Approaches to Evaluate Driving Capacity: Evidence of Congruence and Participants’ Endorsement
Bibliographic record
Abstract
OBJECTIVE: We examined the validity of one-screen versus three-screen driving simulators and their acceptability to middle-aged and older drivers. METHOD: Participants aged 40-55 or 65 and older (N = 32) completed simulated drives first with a single monitor and then with a three-monitor setup, followed by pen-and-paper measures and an interview. RESULTS: Mean differences between one- and three-screen drives were not statistically significant for Starting/Stopping and Passing/Speed. Correlations between the two drives indicated moderate positive linear relationships with moderate agreement. More errors occurred on the one-screen simulator for Signal Violation/Right of Way/Inattention, Moving in a Roadway, Turning, and Total Scores. However, for Moving in a Roadway, Turning, and Total Scores, correlations between drives indicated strong positive linear relationships. We found no meaningful correlation between workload, computer comfort, simulator discomfort, and performance on either drive. Participants found driving simulators acceptable. CONCLUSION: Findings support the use of one-screen simulators. Participants were favorable regarding driving simulators for assessment.
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 | 0.017 | 0.051 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".