Department of Transportation vs Self-reported Data on Motor Vehicle Collisions and Driving Convictions for Stroke Survivors: Do They Agree?
Bibliographic record
Abstract
OBJECTIVE: Research on stroke survivors' driving safety has typically used either self-reports or government records, but the extent to which the 2 may differ is not known. We compared government records and self-reports of motor vehicle collisions and driving convictions in a sample of stroke survivors. METHODS: The 56 participants were originally recruited for a prospective study on driving and community re-integration post-stroke; the study population consisted of moderately impaired stroke survivors without severe communication disorders who had been referred for a driving assessment. The driving records of the 56 participants for the 5 years before study entry and the 1-year study period were acquired with written consent from the Ministry of Transportation of Ontario (MTO), Canada. Self-reports of collisions and convictions were acquired via a semistructured interview and then compared with the MTO records. RESULTS: Forty-three participants completed the study. For 7 (13.5%) the MTO records did not match the self-reports regarding collision involvement, and for 9 (17.3%) the MTO records did not match self-reports regarding driving convictions. The kappa coefficient for the correlation between MTO records and self-reports was 0.52 for collisions and 0.47 for convictions (both in the moderate range of agreement). When both sources of data were consulted, up to 56 percent more accidents and up to 46 percent more convictions were identified in the study population in the 5 years before study entry compared to when either source was used alone. CONCLUSION: In our population of stroke survivors, self-reports of motor vehicle collisions and driving convictions differed from government records. In future studies, the use of both government and self-reported data would ensure a more accurate picture of driving safety post-stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".