Educating Doctors on Evaluation of Fitness to Drive: Impact of a Case-Based Workshop
Bibliographic record
Abstract
INTRODUCTION: In 2004, faced with demographic data predicting large increases in the number of older drivers within a relatively short period combined with the realization that screening for driver fitness was largely dependent on health professionals, principally physicians, the Société de l'assurance automobile du Québec (SAAQ) initiated measures to achieve better cooperation with the health professionals performing the screening. A continuing medical education (CME) program was initiated to improve the health professionals' understanding of road safety considerations. This article describes the program and its impact. METHODS: A 90-minute workshop combining presentation and discussion methods and centering on five case studies was developed and delivered to 824 participants. Outcomes were evaluated at the levels of satisfaction and performance. RESULTS: Participants reported a high level of satisfaction with the workshop. Data suggest that there was an increase in the number of reports submitted by physicians. The quality of physician reports also improved. DISCUSSION: SAAQ statistics show the benefit of its CME program. Informed physicians appear more willing to report drivers with medical problems affecting driver fitness, especially when they are asked to provide functional evaluations and not make decisions about fitness to drive. We believe that the success of this program was due to several factors: (1) its clinical rather than administrative orientation, (2) the use of physicians to deliver the workshop, and (3) formal recognition of the program by the authority responsible for licensing physicians.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".