Alcohol, Trauma and Impaired Driving
Bibliographic record
Abstract
This report has grown out of MADD Canada’s ongoing public education and research projects. First, almost all of these initiatives are based, in part, on an understanding of the adverse consequences and costs of alcohol consumption. Second, many of the projects require that this type of information be marshalled, explained and documented. The more current, comprehensive and authoritative this information, the stronger the projects. This edition of the report is longer and broader in scope than its predecessors. For example, the materials on alcohol consumption and costs, alcohol and fires, and alcohol and the workplace have been expanded. The authors have also included information on alcohol and pilots as well as statistics on drug-impaired driving. The number of charts has been increased and the authors have placed them as close to the relevant text as possible. The primary purpose of this report is to provide a single, referenced source of current facts on alcohol-related trauma. While the authors have focused on Canada, data has also been included on the United States, the United Kingdom, Australia, and to a lesser extent Europe and New Zealand. More information has been included from other jurisdictions when that data was current and comprehensive, or when the Canadian data was not as detailed as the authors would have wanted. Although the international data must be used with caution, it provides a perspective in assessing how well Canada has fared in addressing certain alcohol-related problems. Whenever possible, the authors have relied on the most current and authoritative sources. Preference was given to articles from leading journals, review articles, government sources, and studies from well-recognized organizations, such as the National Highway Traffic Safety Administration, the Canadian Centre on Substance Abuse, the Centre for Addiction and Mental Health, and the Canadian Institute for Health Information. However, it is important to emphasize that the authors did not conduct a comprehensive review of the research literature, apply defined inclusion criteria, or undertake a systematic assessment of the relative quality of the research. Finally, the authors have not attempted to verify the findings that the various sources reported. Despite these limitations, this study should provide a useful resource for those interested in alcohol-related trauma and impaired driving in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".