P3‐419: Strategies to Facilitate Decision‐Making about Driving Cessation for People with Dementia: Perspectives from Healthcare Professionals and Other Stakeholders
Bibliographic record
Abstract
In addition to legal responsibilities for reporting specific diagnoses to government authorities in some jurisdictions, healthcare professionals (HCPs) are often responsible for providing support for driving issues to persons with dementia and their family caregivers in areas such as mobility, social participation and emotional response. Evidence suggests that some HCPs are hesitant to discuss driving with their patients with dementia for a number of reasons, including possible damage to the clinician-patient relationship and lack of training. At the same time, there is a gap in existing research that explores in-depth the resources that are employed by HCPs to assist persons with dementia and caregivers make the transition to non-driving. To fill this gap, qualitative methodology was used to examine the strategies that HCPs use to help to facilitate decision-making about driving, as well as coping and adaptation following driving cessation for persons with dementia and their caregivers. Stakeholders from organizations that represent or provide services to people with dementia and their caregivers were included in the sample in order to broaden perspectives beyond the clinical environment. Semi-structured interviews were conducted with 10 HCPs and 6 other stakeholders. Interviews were transcribed verbatim and entered into NVivo (version 9) software program for analysis. We employed inductive analysis techniques to generate descriptive themes around facilitating driving cessation. The findings to date include four broad themes: 1) The need to promote access to programs, resources and services to enable continued mobility and social participation; 2) The need to integrate driving cessation resources and tools within broader support systems that address the psychosocial aspects of dementia; 3) Gaps in emotional and psychological supports; and 4) The need for a balance between standardized approaches that can be employed widely, with those that target distinctive individual and local contexts. This study indicates that there are gaps in support for persons with dementia and their family caregivers with respect to driving cessation. Findings emphasize the importance of addressing these gaps while providing practical support through educational resources and social and alternative transportation programs in order to facilitate coping with driving cessation.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".