A comparative study of blood alcohol concentrations in Australian night-time entertainment districts
Bibliographic record
Abstract
INTRODUCTION AND AIMS: There is little research describing how intoxication levels change throughout the night in entertainment districts. This research aims to describe levels of alcohol intoxication across multiple Australian metropolitan and regional nightlife districts. DESIGN AND METHODS: This study was conducted in the night-time entertainment districts of three metropolitan cities (Sydney, Melbourne and Perth) and two regional cities (Wollongong and Geelong) in Australia. Data collection occurred approximately fortnightly in each city on a Friday or Saturday night between 8 pm and 5 am. Brief structured interviews (3-10 min) and breathalyser tests were undertaken in busy thoroughfares over six months. RESULTS: Of the 7037 individuals approached to participate in the study, 6998 [61.8% male, mean age 24.89 years (standard deviation 6.37; range 18-73)] agreed to be interviewed. There was a linear increase in blood alcohol concentration (BAC) levels throughout the night. Post hoc testing revealed significantly more highly intoxicated participants (i.e. BAC above 0.10 mg of alcohol per 100 mL of blood) after midnight (P < 0.05). The overall mean BAC was 0.06 mg/100 mL. Men were more intoxicated than women earlier in the night, but gender differences disappeared by 3 am. There was no age differences in intoxication earlier in the night, but after midnight, patrons over the age of 21 showed increasing BAC levels. DISCUSSION AND CONCLUSIONS: There is a consistent trend across the cities of high to very high levels of intoxication later in the night, with trends after midnight being significantly different to those before.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".