Voluntary declaration of a medical condition by drivers in Quebec
Bibliographic record
Abstract
As is the case in most North American jurisdictions, Quebec requires drivers to self-report any medical conditions that may affect driving. Doubts have been expressed as to the willingness of drivers to self-report medical conditions when doing so may have a negative effect upon their permit status. In 2008, the Commission d'Acces a l'Information gave the Societe de l'Assurance Automobile du Quebec (SAAQ) permission to create a databank incorporating medical data on all the drivers in Quebec with the data already held by the SAAQ in order to be able to study the effects of the severity of drivers' medical conditions upon crash risk. The resulting databank contains billing data for physicians in addition to hospitalisation and medications data obtained from the Regie d'Assurance Maladie du Quebec (RAMA) and the Ministry of Health and Social Services (MSSS) for all drivers aged 16 or more for the period 1 July 2003 - 30 June 2006 for whom a positive relationship with the medical data could be established. The objective of this phase of the Quebec study is to determine if the drivers who have been diagnosed with a medical condition that is included in the SAAQ medical questionnaire report their medical condition to the SAAQ as required by law. Additionally, for the drivers over 75 years, their voluntary reporting of their medical conditions will also be compared with the information supplied by physicians during their compulsory age-based medical reviews. The results show that there is serious underreporting of medical conditions considered to negatively affect driving by drivers using the Quebec voluntary declaration form. Underreporting of these conditions ranges from 84% to 99%. At least 85% of voluntary declarations concern conditions that have little or no effect upon permit status and 79.59% are to the effect that the driver wears spectacles or contact lenses to drive. Many drivers with a condition that may influence driving report their more benign conditions while omitting to report the condition that could affect their permit status.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".