Automated vehicles as a tool to extend the safe driving of people with dementia: Family caregivers’ perspectives
Bibliographic record
Abstract
Abstract Background The progression of dementia often leads to complete driving cessation, which poses major challenges for persons with dementia (PwD) and their caregivers. In response to these challenges, the use of Automated Vehicles (AV) by PwD has been considered as a way of prolonging PwD’s safe driving. AVs can either be used to assist PwD with certain driving tasks, such as steering or braking (Partially Automated Vehicles; PAVs), or by performing all driving tasks (Fully Automated Vehicles; FAVs). There are unique considerations regarding the use of AVs by PwD that are currently not well‐understood. This study examined caregivers’ perspective on the usefulness of AVs in addressing the driving‐related challenges faced by PwD. Method Semi‐structured interviews were conducted with 20 primary family caregivers of PwD. Both in the interviews and using a Likert scale questionnaire, participants were asked about their attitude towards PAV and FAV use by themselves and the PwD in their care. Thematic analysis with inductive coding was used to analyse the transcribed interview data. Result Caregivers reported significantly more negative attitudes towards PAV/FAV use by the PwD in their care compared to use by themselves (Table 1). The thematic analysis yielded two overarching types of caregiver concerns. (1) unresolved concerns about PwD’s mobility that persist after PAV/FAV use: difficulty navigating tasks at the destination; AVs not providing the same sense of freedom as driving; need for caregivers’ presence in the vehicle; caregivers’ unawareness of PwDs’ driving ability decline until a traffic incident. (2) emerging concerns specific to PAV/FAV use by PwD: PwD’s confusion caused by lack of exposure to AVs; PwD’s possible distress/agitation in AVs; PwD’s possible inability to navigate tasks required by the AV (e.g., response to system failures, negotiating pick‐up/drop‐off locations); PwD’s manual driving skill degradation upon constant use of AVs; AVs enabling PWD to wander to distant locations. Conclusion This study helps to identify AV design targets specific to PwD. In addition, study results outline caregivers’ concerns around AV use by PwD that extend beyond PwD’s driving, which highlights the importance of considering a holistic perspective when addressing mobility‐related needs of PwD by introducing AVs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".