Driving and Dementia in Ontario: A Quantitative Assessment of the Problem
Bibliographic record
Abstract
BACKGROUND: The population is becoming increasingly aged, and concomitantly, the prevalence of dementia is steadily rising. Persons aged 65 years and over are likely to continue driving for many years and often well into the dementia process. METHODS: Ontario Ministry of Transportation driving data, census data, and dementia prevalence data were combined to determine the number of persons with potential dementia who are driving, both now and in about 25 years' time. RESULTS: Actual and projected Ontario figures show that the number of senior drivers will increase markedly from just under 500,000 in 1986 to nearly 2,500,000 in 2028. Similarly, the number of drivers with dementia is also increasing. Although not all drivers with dementia are necessarily dangerous, most are estimated to continue driving well into the disease process. By combining the above-mentioned data sets, a best estimate of the number of drivers with dementia in Ontario was derived. It is estimated that this group has grown from just under 15,000 in 1986 to about 34,000 in 2000 and will number nearly 100,000 in 2028. INTERPRETATION: Increasingly, the responsibility for identifying drivers with dementia has fallen on the health care system, a role for which it was never designed nor equipped to handle. The risks associated with the dramatically increasing number of drivers with dementia demand a psychometrically sensitive and efficient screening procedure.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".