Bibliographic record
Abstract
Dear Editor: In the January 2005 pissue of the Canadian Journal of Psychiatry, risk assessment in psychiatric practice is reviewed. Four helpful articles provide general psychiatrists with an up-to-date, current overview of forensic practice and risk assessment. In particular, Clinical Use of Risk Assessment, by Graham Glancy and Gary Chaimowitz, carefully reviews the current practice of acting on a risk assessment and the duty to protect a specific population at potential risk of physical threat or injury (1). But what about the risk to the general population from forensic patients as a result of their dangerous driving behaviours? Under the 2000 Canadian Medical Association guidelines (2), general physicians as well as general psychiatrists are mandated to report drivers with psychiatric illness to their local ministry of transportion when the illness is thought to interfere with their ability to drive a motor vehicle safely. Unfortunately, no clinically useful instruments are readily available to guide clinicians in this important risk assessment. Current findings from a metaanalysis of the world literature on driving risk and psychiatric illness reveals significant findings related to substance use but a deficit in other diagnostic categories that are relevant to forensic psychiatry (3). In particular, there are no significant data available to guide clinicians regarding the degree of risk associated with a diagnosis of antisocial personality disorder and driving. This lack of available evidence-based data relating to psychopathy is compounded by inherent difficulties faced by forensic psychiatrists in clinical practice. There appears to be an inherent conflict in asking leading questions regarding potentially dangerous driving styles that may inhibit open disclosure about other more immediately relevant clinical forensic issues. McGill University is currently conducting a Canadian-based survey on psychiatrists' knowledge of and practice in psychiatric illness driving and reporting styles (4). It would be interesting to compare a representative group of forensic psychiatrists' current practice of reporting high-risk drivers, during the course of their clinical practice, with a sample of more general psychiatrists not engaged in forensic work. The development of a simple screening device for problem driving would be useful to all physicians in everyday practice. Such an instrument might have a place in the overall forensic risk assessment. As Glancy and Chaimowitz note in their article, Further instruments should be established and well validated (1, p 13). We are currently developing a clinical screening instrument, the Jerome Driving Questionnaire, that we hope will be useful in general clinical practice, as well as in more specialized areas such as forensic work. Preliminary data indicate that this instrument shows clinically useful correlations with on-the-road driving assessments, made by experienced driving instructors, of driving risk in nonclinical populations of novice drivers (5). References 1. Glancy G, Chaimowitz G. The clinical use of risk assessment. Can J Psychiatry 2005;50:12-7. …
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".