The effect of the <i>Safer Bars</i> programme on physical aggression in bars: results of a randomized controlled trial
Bibliographic record
Abstract
The purpose of this study was to evaluate the effectiveness of Safer Bars, an intervention to reduce aggression in bars. A total of 734 pre - post-intervention observations were conducted by trained observers on Friday and Saturday nights between midnight and 2 a.m. in 18 large capacity ( > 300) Toronto bars and clubs assigned randomly to receive the intervention (69% participation rate of the 26 assigned) and 12 control bars. As part of the intervention, owners/managers completed the risk assessment workbook to identify ways of reducing environmental risks, and 373 staff and owners/managers (84% participation rate) attended a 3-hour training session focused on preventing escalation of aggression, working as a team and resolving problem situations safely. The main outcome measures were rates of severe aggression (e.g. punching, kicking) and moderate physical aggression (e.g. shoving, grappling). Hierarchical linear modelling (HLM) comparing pre - post aggression for intervention versus control bars indicated a significant effect of the intervention in reducing severe and moderate aggression. This effect was moderated by turnover of managers and door/security staff with higher post-intervention aggression associated with higher turnover in the intervention bars. The findings indicate the potential for a stand-alone relatively brief intervention to reduce severe and moderate physical aggression in bars.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".