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The impact of later trading hours for hotels (public houses) on breath alcohol levels of apprehended impaired drivers

2007· article· en· W1941773469 on OpenAlexaff
Tanya Chikritzhs, Tim Stockwell

Bibliographic record

VenueAddiction · 2007
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDemographyClosing (real estate)ApprehensionMedicinePsychologyGerontologyEnvironmental healthBusinessSociologyFinance

Abstract

fetched live from OpenAlex

AIM: To examine the impact of extended trading permits (ETPs) for licensed hotels in Perth, Western Australia on impaired driver breath alcohol levels (BALs) between July 1993 and June 1997. DESIGN: Forty-three hotels obtained ETPs allowing later closing hours and 130 maintained standard closing time (controls). Impaired driver BALs were linked to 'last place of drinking' hotels. Before and after period BALs of drivers who last drank at ETP or non-ETP hotels were compared by time of day of apprehension and sex, controlling for age. FINDINGS: Impaired female drivers apprehended between 10.01 p.m. and 12 midnight (before closing time) had significantly lower BALs after drinking at ETP hotels. Male drivers aged 18-25 years and apprehended between 12.01 and 2.00 a.m. after drinking at ETP hotels had significantly higher BALs than drivers who drank at non-ETP hotels. CONCLUSIONS: At peak times for alcohol-related offences, late trading is associated with higher BALs among those drinkers most at risk of alcohol-related harm.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.060
GPT teacher head0.326
Teacher spread0.266 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

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