Medications and driving: community knowledge, perceptions and experience
Bibliographic record
Abstract
There is increasing awareness of the potential for any medication that acts on the central nervous system to impair judgement and motor functioning, including driving performance. This paper reports community knowledge, perceptions and experience in relation to driving while taking medications. A community-based survey (n=316) revealed that of those who had taken any type of medication in the last 7 days (n=193), a quarter (24%) had driven while taking a medication that they thought could affect them. Of those who drove for work, a quarter (26%) of the respondents reported that they had changed or stopped their work-related driving because they were taking a medication that displayed a warning label about driving. Outside of work, a third (35%) of the total number of respondents reported that they had done so. Of those who had taken any type of medication in the last 7 days, 62 were taking on a daily basis one or more medications classified as being likely to have a warning label about driving, such as sedatives, tranquilizers, antidepressants, analgesics and anticonvulsives. This paper will examine community knowledge, perceptions and experience surrounding medications and driving with particular reference to those persons who were taking drugs with a warning label, and the barriers to following such warnings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".