Developing a Canadian-Specific Version of the Fitness-to-Drive Screening Measure <sup>©</sup>
Bibliographic record
Abstract
UNLABELLED: The Fitness-to-Drive Screening Measure(©) (FTDS) is a valid and reliable screening tool that identifies at-risk older drivers. Although 12,300 Canadians have used the FTDS in the last 2 years, the resources/recommendations targeted the U.S. CONTEXT: The objective of this article is to identify the FTDS resources/recommendations appropriate for Canadian users and the barriers that Canadian stakeholders experience when promoting older driver fitness. Twenty stakeholders from three provinces (eight occupational therapists, three certified driver rehabilitation specialists, four physicians, and five members of advocacy organizations) participated in semi-structured interviews. We conducted summative and thematic content analysis. A comprehensive set of resources/recommendations was identified. Barriers to older driver fitness decisions included fear of losing the license, compromising the physician-client relationship, insufficient training/resources for health care professionals, and inadequate alternative transportation. Canadian context-specific resources/recommendations were integrated into a Canadian version of the FTDS. This version may better serve Canadian older drivers, caregivers, and health care professionals.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".