A Systematic Review of the Risks of Motor Vehicle Crashes Associated with Psychiatric Disorders
Bibliographic record
Abstract
OBJECTIVE: Psychiatric disorders and their treatments have the potential to adversely impact driving skills. However, it is unclear to what extent this poses a public health risk by increasing the risk of motor vehicle crashes (MVCs). The aim of this systematic review was to synthesize and critically appraise evidence on the risk of MVC for drivers with psychiatric disorders. METHOD: We conducted a systematic review of the MVC risk associated with psychiatric disorders using seven databases in November 2019. Two reviewers examined each study and extracted data. The National Heart, Lung, and Blood Institute Quality Assessment tools were used to assess each study's quality of evidence. RESULTS: We identified 24 studies that met the inclusion criteria, including eight cohort, 10 case-control, and six cross-sectional designs. Quality assessment ratings were "Good" for four studies, "Fair" for 10, and "Poor" for 10. Self-report or questionnaires were used in place of objective measures of either MVC, psychiatric disorder, or both in 12 studies, and only seven adjusted for driving exposure. Fifteen studies reported an increased risk of MVC associated with psychiatric disorders, and nine did not. There was no category of disorder that was consistently associated with increased MVC risk. CONCLUSION: The available evidence is mixed, not of high quality, and does not support a blanket restriction on drivers with psychiatric disorder. An individualized approach, as recommended by international guidelines, should continue. Further research should include objective assessments of psychiatric disorders and MVC risk and adjust for driving exposure.
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".