Bibliographic record
Abstract
Dear Editor, I read the AFPI position paper on road safety and public health with interest.[1] The authors mentioned the use of alcohol, co-morbid medical conditions (diabetes mellitus, Parkinson's disease, Alzheimer's disease, epilepsy), and adverse drug reactions among the risk factors for road traffic accidents. Psychiatric illnesses are also an important risk factor for road traffic accidents. It is well known that many psychiatric disorders can lead to impairment in the level of cognitive and executive functioning required for safe driving, and medications used to treat them can also potentially cause disruption in perception, information processing, and overall psychomotor activity.[23] Moreover, studies have suggested that drivers with mental health conditions have a higher risk of being involved in a crash.[4] A recent systematic review tried to identify what is known about driving for people with mental health conditions, and critically appraise studies that empirically investigated assessment of fitness-to-drive among people with mental health conditions revealed many interesting findings.[5] Among patients with schizophrenia, even when stabilized with antipsychotic medication, great proportion of the patients were reported not fit-to-drive.[5] Among patients with major depressive disorder higher levels of sleepiness were found when driving, irrespective of medication use.[5] Moreover, depressive patients were also found to have slower steering reaction times and a greater number of car crashes when compared with controls.[5] Statistically higher crash rates were also identified in personality disorder group and in the psychoneurotic group when compared with controls.[5] However, the authors concluded that the overall quality of studies examining fitness-to-drive is low and large-scale longitudinal studies with age-matched controls are urgently needed in order to determine the effects of different conditions on fitness-to-drive.[5] Considering the above findings, it is important to assess each patient with psychiatric disorders to determine if the patient is fit-to-drive to reduce the risk of road traffic accidents. A study from the United Kingdom exploring whether the mental health practitioners were assessing their patients’ fitness-to-drive and addressing the issue as guided by the relevant agencies and legislation found that there was a poor compliance with the standards among assessing clinicians.[6] Another study exploring the practices of Canadian psychiatrists regarding fitness-to-drive in individuals with mental illness found that only 18.0% of respondents were always aware of whether their patients were active drivers.[7] The above study results indicate that there is a clear need for education and guidelines to assist psychiatrists in decision making about driving fitness. Though, there is no single assessment that can be used to accurately predict driving ability of people with psychiatric illnesses, it is recommended that a series of assessment methods including medical and occupational therapy assessments, neuropsychological tests, on-road assessment, and car driving simulator tests should be used to reach a conclusion regarding fitness-to-drive.[5] The Driver and Vehicle Licensing Agency (DVLA) in the United Kingdom also provides clear and detailed recommendations on minimum stand-down periods from driving relating to various psychiatric conditions.[8] Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".