Survey guidelines to assess driver alcohol and drug use
Bibliographic record
Abstract
Roadside surveys of alcohol use among drivers have been used for many years to measure the prevalence of alcohol use among drivers. A standard protocol for these surveys is required to compare results across jurisdictions and/or over time. The objective of this project was to describe a standard protocol for conducting a roadside survey to determine the prevalence of alcohol and drug use among nighttime drivers. In addition, the document addresses many of the issues and questions that arise when a roadside survey is being considered and provides an overview of many of the steps required to help ensure a successful project. A roadside survey is a major effort that requires considerable forethought, planning, negotiations with key stakeholders and partners, and the development of a detailed protocol for the survey. It is an intensive effort that requires a tremendous amount of preparation. The key to a successful project is careful planning and a standard protocol will provide guidance in this process. This protocol has been developed over time and modified to add drug collection and examine the use of daytime sites. These procedures have been tested and improved in multiple surveys conducted in Canada over past decades. The result is a protocol that addresses key issues and concerns and provides valid measurements of general alcohol and drug use on a jurisdictionrs roads which can be monitored overtime or used as a before and after measurement system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".