Cognitive screening tools for predicting unsafe driving behavior among senior drivers
Bibliographic record
Abstract
As Canada’s elderly continue to represent the fastest growing population in Canada, there has been an increasing need for effective and efficient screening tools for senior drivers, especially ones which identify possible cognitive impairments. Thus, the objective of this study was to conduct a meta-analysis of the available research surrounding the predictive value of pencil-and-paper cognitive screening tools. A systematic review of existing literature was conducted, with a final sample of 15 evaluation outcomes that identified 10 different pencil-and-paper tools. Multiple techniques were used to evaluate the data, including random effects modeling, meta-regression analysis and tests for bias, including publication bias. Finally, a multilevel meta-regression model was used to account for dependence of evaluation outcomes coming from the same study. A small to medium-sized significant pooled effect of 1.94 was found, indicating that when pencil-and-paper cognitive screening tools predict a driver is unsafe, there is a 94% greater chance that this driver will exhibit unsafe driving behaviors. Results, however, only provide partial evidence to inform the selection of pencil-and-paper cognitive screening tools, as it was not possible to unequivocally identify which tool performed best.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".