Drivers with dementia: Forecasting the future
Bibliographic record
Abstract
BackgroundA decline in driving skills is well documented in people with dementia.ObjectiveTo provide a current estimate and future forecast of drivers with dementia in Ontario, Canada, taking into account sex differences and longitudinal estimates of driving cessation in dementia.MethodsWe used historical provincial licensing data, population estimates and projections, as well as estimates of diagnosable dementia from the Landmark study of the Alzheimer's Society of Canada to create current estimates and forecasts of drivers with dementia in the province of Ontario, the most populous province of Canada, from 2024 to 2046. Sensitivity analyses were used to determine the impact of sex and assumptions regarding the rate of driving cessation.ResultsAssuming that an estimated 35% of people with diagnosable dementia stop driving very shortly after symptom onset followed by a more gradual decline over time, and that females stop driving twice as fast as men, we forecast approximately 154,000 drivers with dementia in the province of Ontario in 2046.ConclusionsAs dementia prevalence increases, our study provides a novel set of projections for drivers with dementia over the coming two decades, estimating a 221% to 226% increase. This work adds to the myriad of concerns about health and public services that will be needed to treat and support this population effectively, to detect early signs of dangerous driving among the cognitively impaired, and to provide alternative transportation options, once driving is no longer viable.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".