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Record W617788592

Alcohol, Trauma and Impaired Driving

2006· article· en· W617788592 on OpenAlexaboutno aff
Christina Bryant, Lisa Gibbs, E Usprich, Lynn A. Crosby, S Kettle, C Zahnleiter, R Solomon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Perspective (graphical)Alcohol consumptionConsumption (sociology)Human factors and ergonomicsPoison controlMedicineSuicide preventionPolitical sciencePublic relationsEnvironmental healthPsychologyBusinessAlcoholComputer scienceSociologySocial science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.010
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.022
GPT teacher head0.272
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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