Driving and Dementia: Workshop Module on Communicating Cessation to Drive
Bibliographic record
Abstract
BACKGROUND: For persons with dementia (PWD), driving becomes very dangerous. Physicians in Canada are legally responsible to report unfit drivers and then must disclose that decision to their patients. That difficult discussion is fraught with challenges: physicians want to maintain a healthy relationship; patients often lack insight into their cognitive loss and have very strong emotional reactions to the loss of their driving privileges. All of which may stifle the exchange of accurate information. The goal of this project was to develop a multimedia module that would provide strategies and support for health professionals having these difficult conversations. METHODS: Literature search was conducted of Embase and OVID MedLine on available driving and dementia tools, and on websites of online tools for communication strategies on driving cessation. A workshop module was developed with background material, communication strategies, links to resources and two videos demonstrating the "bad" then the "good" ways of managing this emotionally charged discussion. RESULTS: < .001), and comfort and willingness in discussing the subject improved. CONCLUSION: This project demonstrated the positive impact of the module on improving health professionals' attitude and readiness to communicate driving cessation to PWD.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".