SAFER BARS: REDUCING VIOLENCE IN AND AROUND LICENSED PREMISES
Bibliographic record
Abstract
Background The Safer Bars programme, developed in Canada in the late 1990's, resulted in a 30% reduction in violent incidences in bars where it was trialled in Canada. The Injury Control Council of WA (ICCWA) ran a successful pilot with venues in the entertainment precinct in the City of Vincent. Aim To work with licensed venues to implement changes to reduce the incidence of violence, aggression and injury by improving staff skills in communication within their staff groups and between staff and patrons. Method The methodology of the programme was primarily centred around a delivered training package and observational audits at 3, 6 and 12 month intervals. Police offence data was also recorded early in the project to identify a baseline and then again intervals post initial training. Outcome The programme yielded positive results, recorded very high satisfaction rates and positive learning outcomes by participants. In particular, the module ‘Responding to Problem Situations’ was the highest rated training module with 93% of participants saying that they found it useful or extremely useful. This is very encouraging for the programme as this is one of the central aspects of Safer Bars. Significance Safer Bars is an effective measure to reduce alcohol related crime and violence in and around licensed venues.
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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".