Fitness-to-Drive Screening Measure©: Patterns and Trends for Canadian Users
Bibliographic record
Abstract
Background: The Fitness-to-Drive Screening Measure© (FTDS) is an online screening tool that enables proxy raters (caregivers, family members, and friends) to identify at-risk older adult drivers via 54 driving-related items. This study aimed to identify areas in need of improvement for the FTDS by identifying the patterns and trends of Canadian users and providing recommendations to increase the usage, reach, and potential impact of the FTDS as a health promotion tool. Methods: We used monthly Google Analytics reports to calculate descriptive statistics for web page and session specific variables. Variables were separated into Year 1 and Year 2 and were compared using the independent sample t-test. Results: Patterns were identified for session and web page specific variables; for example, users spent less than the recommended 20 min to complete the FTDS. There was only a significant decrease in the number of French speaking users (t (22) = .01, p < .05) from Year 1 to Year 2. Conclusion: Canadians across the country are able to easily access and use the FTDS for screening older adult drivers in its current format. However, implementing suggested recommendations (e.g., short form FTDS) may increase the overall usage, utility, and/or reach of the FTDS, and, as such, may yield additional benefits to potential users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".