Incidents of harm in European drinking environments and relationships with venue and customer characteristics
Bibliographic record
Abstract
Quigg, Z., Hughes, K., Bellis, M., van Hasselt, N., Calafat, A., Košir, M., Duch, M., Juan, M., Voorham, L., & Goossens, F. (2014). Incidents of harm in European drinking environments and relationships with venue and customer characteristics. The International Journal Of Alcohol And Drug Research, 3(4), 269-275. doi:http://dx.doi.org/10.7895/ijadr.v3i4.180Aim: Research shows there are associations between bar environments and alcohol-related harms. However, few European studies have examined such links. Our study investigates the type of harms experienced by patrons in European bars, and their relationships with individual, social and environmental factors.Design: Unobtrusive one-hour observational visits. Characteristics of the bar environment, staff and patrons, and harms observed were recorded on structured schedules.Setting: Bars in four cities in the Netherlands, Slovenia, Spain and the United Kingdom (U.K.).Participants: 238 observations across 60 bars.Measures: Analyses utilized chi-squared, analyses of variance and logistic regression.Findings: 114 incidents of harm were observed; in one-fifth of visits, at least one incident was recorded. People falling over, arguing or being so severely intoxicated that they required assistance to walk were the most common incidents observed. Bivariate analyses showed associations between a range of staffing, customer and environmental characteristics, and incidents of harm. Controlling for city and venue, only a permissive environment remained significant in multivariate analyses.Conclusions: Harms occurring in nightlife venues are typically minor. However, such incidents have the potential to escalate into more serious harms; thus, prevention is crucial. Prevention should focus on improving venue management practice and on the behavioral standards expected of customers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".