Bibliographic record
Abstract
Over the past several decades, the impairing effects of alcohol on driving have become common knowledge. More recently, the use of illicit drugs such as cocaine, cannabis and methamphetamine have become the focus of increasing concern for its impact on road safety. However, it is less well understood that some psychoactive prescription drugs can also affect driving. Psychoactive prescription drugs, such as opioids, sedative-hypnotics and stimulants, are associated with serious harms including injury and death. In an effort to address these and other harms, the Canadian Centre on Substance Abuse (CCSA), together with over 40 partners, released First Do No Harm: Responding to Canada’s Prescription Drug Crisis, a 10-year pan-Canadian strategy that outlines 58 recommendations for collective action in a number of key areas, including prevention, education, treatment, monitoring and surveillance, enforcement, and legislation and regulation. The current review explores the extent to which psychoactive prescription drugs can adversely affect the cognitive and motor functions essential for the safe operation of a motor vehicle and thereby increase the risk of crash involvement. More specifically, the objectives of this report are: • To review and summarize the scientific literature on the impairing effects of psychoactive prescription drugs on the skills and abilities required to operate a vehicle safely; • To examine the epidemiological evidence on the extent to which psychoactive prescription drugs are used by drivers and increase the risks of crash involvement; and • To identify approaches for enhancing the safety of drivers who use psychoactive prescription drugs in Canada. The evidence reviewed in this report will help to inform policies and practices aimed at reducing injuries associated with driving impairment involving psychoactive prescription drugs.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".