Driving and dementia: An “emotionally‐charged” issue for people with dementia and their multidisciplinary healthcare providers
Bibliographic record
Abstract
Abstract Background The decision to stop driving and the transition to non‐driving is emotional and challenging not only for people with dementia (PWD) and their family members, but also for healthcare providers (HCPs) of multiple disciplines. With the growing number of older drivers, including drivers with dementia, HCPs are being increasingly tasked with addressing driving cessation issues. However, research indicates that most HCPs lack the knowledge, skills and resources to support PWD and their families in this context, often resulting in their avoidance of this issue. Our objective was to explore HCPs’ perspectives on the content they deemed important to include in an e‐learning program being developed to educate HCPs about dementia and driving. Method In‐depth semi‐structured interviews were conducted with 22 HCPs, including six primary care physicians, three geriatricians, three geriatric psychiatrists, six nurse practitioners and four occupational therapists practicing in six Canadian provinces. Participants were asked to provide feedback on the proposed content outline of an e‐learning program. Data were examined using a qualitative thematic analysis approach. Results Participants emphasized the “emotionally‐charged” nature of driving cessation, which they identified as being extremely challenging for PWD, their families and for HCPs. The trauma and negative impact that driving cessation has on PWD also affects HCPs’ emotions and work stress, influencing how they navigate driving cessation with their patients/clients. Further contributing to some HCPs’ stress was a lack of knowledge and confidence about determining when drivers were unsafe to drive and how to support them through the transition to non‐driving. Protecting the therapeutic relationship was a primary concern for HCPs, which they managed by: 1) employing avoidance and referral practices; and 2) mitigating PWD’s and family members’ emotional responses via communication strategies (e.g. deep understanding, prepared scripts, early and on‐going discussions about driving). Conclusion Study results highlight the importance of addressing the inter‐related emotional aspects of driving cessation for both PWD and HCPs in educational programs directed to HCPs.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".