39 Violence intervention and prevention in emergency rooms: the viper study
Bibliographic record
Abstract
Background Nearly half a million people are killed each year as a consequence of intentional injury and many more seriously injured. Information sharing by Emergency Departments with local government for crime prevention purposes has previously been described as an effective, cost effective, low cost and low risk intervention for tackling community violence. The UK Department of Health and the Home Office support this process known as the Information Sharing to Tackle Violence (ISTV) program. In 2007, we implemented ISTV and have witnessed a substantial reduction in assault related attendances. We aimed to examine the epidemiology of our assault victims over the introduction period. We hypothesised a priori that there would be reductions in the proportions of patients who attended at the weekend and late at night, reductions in the proportions of young adult males, and reductions in the more seriously injured patients. Methods We extracted all the cases of assault from our patient administration system between the 1 st of January 2005 and the 31 st of December 2014 and analysed the data using Poisson regression. To estimate the trend over the 10 year period, we used indicator variables for each year (other than 2005 which was treated as the reference category) to allow for non-linearity. Deviance tests of the models were used to assess whether the inclusion of the interaction terms improved model fit. When there was evidence of statistically significant interactions, we re-ran the model looking at the rate of change over time separately for each strata of the effect modifier. Results Over the study period, the rate of emergency department presentations following an assault decreased up until 2014, at which point it was 37% lower (IRR=0.63; 95% CI:0.57 to 0.69) than in 2005. There were greater reductions for presentations on weekends (IRR=0.57; 95% CI:0.50 to 0.64) versus weekdays (IRR=0.72; 95% CI:0.62 to 0.83); presentations with no hospital follow-up (IRR=0.51; 95% CI:0.45 to 0.56) versus those leading to either an inpatient (IRR=1.05; 95% CI:0.84 to 1.31) or outpatient (IRR=1.23; 95% CI:0.93 to 1.64) follow-up over the 10 year period. Conclusions The ISTV program in this single ED has been associated with substantial reductions in violence related presentations. This effect is most marked in presentations at the weekend, younger patients and in those who appear to be least seriously injured.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".