Impaired driving risk assessment of first and repeat offenders and knowledge transfer in Canada
Bibliographic record
Abstract
The purpose of this research was to provide: i) an overview of risk assessment practices for impaired drivers across Canadian driver licensing and criminal justice systems, ii) a snapshot of knowledge among practitioners, and iii) insight into barriers that inhibit knowledge transfer and ways to address them. Focus groups and key informant interviews were conducted with administrative impaired driver program staff and criminal justice professionals in five Canadian jurisdictions. A small survey of justice professionals representing six jurisdictions was also conducted. Results were analyzed using a Delphi panel approach and peer review. Results identified some strengths with risk assessment practices in the driver licensing system; fewer strengths were evident in the justice system. Concerns about current risk assessment practices include training, types of instruments used, caseloads, and the measurement of program outcomes and effectiveness. Strategies to improve practices and overcome barriers to knowledge transfer were recommended. More research on first and repeat impaired driver risks and risk assessment instruments is needed. Increased efforts among researchers to disseminate research to practitioners, or partner with those who can, would strengthen risk assessment practices. This is essential given the number of impaired drivers processed annually and the profound costs of delivering interventions that fail to address the risk that offenders pose.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".