Controlling alcohol‐related violence: a treatment programme
Bibliographic record
Abstract
INTRODUCTION: Control Of Violence for Angry Impulsive Drinkers (COVAID) is a structured, cognitive-behavioural treatment programme for people in the community. The importance of the programme is that it addresses the link between two major problems areas--drinking and aggression--while emphasizing the reduction of the latter. AIM: To conduct a pilot study of the effectiveness of COVAID. METHOD: Six COVAID participants were assessed using psychometric measures and self-reported alcohol consumption and aggression, before and after a 10-session COVAID programme. They and 10 other men regarded as potentially suitable but who had not completed COVAID were compared for reconviction over a period of 18 weeks from referral. RESULTS: Six of the 17 referrals to COVAID completed the programme; one was not accepted for the programme, one is still in treatment, three became unavailable for COVAID, three did not attend the first interview, and three dropped out of treatment. The six completers showed improvement on alcohol-related aggression beliefs, social problem solving, anger control and impulsiveness. Improvements in alcohol consumption were not uniformly observed, although self-reported aggression was low. Reconvictions for violence were lower in the COVAID group (one reconvicted out of six men) compared with those referred but who did not participate in COVAID (three reconvicted out of 10 men). DISCUSSION: This preliminary information shows that COVAID holds promise as an intervention for alcohol-related aggression and violence. While the indicators are positive, given the small numbers, the short follow-up period and the lack of an adequate control group, further evaluation is necessary. Given the difficulties in recruiting suitable candidates, a multi-centre study is recommended.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".