Driving, Navigation, and Vehicular Technology: Experiences of Older Drivers and Their Co-Pilots
Bibliographic record
Abstract
OBJECTIVE: The objective of this article is to explore relationship between older drivers and their passengers (co-pilots) and potential implications of in-vehicle navigation technology on their driving safety. METHODS: Semi-structured interviews were conducted with 44 healthy, community-dwelling older adults (aged 60-83) or 22 married couples. Males identified themselves as drivers and females identified themselves as passengers (i.e., co-pilot). RESULTS: Findings indicate that operating a motor vehicle in older adulthood is a shared activity between drivers and passengers. Older drivers and co-pilots reported their level of interaction depended on their familiarity with their route. Navigating unfamiliar areas, particularly large urban centers, was identified as the most challenging driving situation. Participants identified their level of collaboration would increase with the advent of in-vehicle navigation technology. Safety concerns related to the use of this technology, included distraction of both drivers and passengers. Differences amongst couples in their perceptions of using this technology were linked to their level of experience with using other forms of technology. CONCLUSIONS: Older drivers and passengers identified working closely together when operating a motor vehicle. Further investigation into the effects of in-vehicle navigation technology on the driving safety of older drivers and their co-pilots is 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".