The relationship between homicide and alcohol, drugs and psychiatric disorder: some directions for prevention
Bibliographic record
Abstract
A paper published by Shaw et al. [1] in this issue of Addiction is based on an unusual and impressive data set that includes psychiatric reports for almost three-quarters of homicide perpetrators over a 3-year period. From these reports, the investigators extracted information on clinical history of the perpetrator (alcohol and drug dependence or misuse, psychiatric disorder) and mental state at the time of the offense, including the extent that alcohol or drugs were judged likely to have influenced the homicide. While the data are potentially very useful, I find the results as presented difficult to interpret because the groups compared in Table 1 of their report are not mutually exclusive, including substantial overlap of the alcohol and drug misuse groups. The second problem is that the total sample comparison group appears to include the 25% who were not given a psychiatric evaluation; yet statistics for variables such as psychiatric disorders seem to have used the total sample as the denominator rather than the number who had a psychiatric evaluation. The findings would have been easier to interpret if the comparisons had been made for the following mutually exclusive categories: (1) alcohol dependence/misuse only; (2) drug dependence/misuse only; (3) dependence/misuse of both alcohol and drugs; (4) psychiatric report with no history of alcohol/drug dependence/misuse; and (5) no psychiatric report (including only variables that were available without a psychiatric report). Despite these limitations, however, some patterns emerged related to alcohol and drugs. Consistent with other research [2], homicides either involving alcohol/drugs and/or by perpetrators who have a history of alcohol/drug problems were more likely to involve males who were strangers to each other. Alcohol/drug perpetrators were also more likely to be unemployed and have certain types of psychiatric disorders. Thus, the pattern that emerges is one of marginalization—that is, those committing alcohol or drug-related homicides are more marginalized from mainstream society (i.e. unemployed, unmarried, serious psychiatric disorder, conflicts with strangers), and this is especially true for drug-related homicides. Although homicides are generally often the result of escalation of private conflicts between friends, family and spouses, this appears to be less the case for alcohol- and drug-related homicides. Therefore, there may be greater opportunities for prevention of alcohol/drug related violence by focusing on where, when and under what circumstances alcohol- and drug-related crimes are committed using a situational crime prevention approach [3]. Another notable factor that emerges from this research is the high rate of previous involvement with treatment or legal systems (i.e. previous convictions for violence and contact with services) among alcohol/drug offenders. This finding highlights the need for the development of more effective rehabilitation programs for alcohol/drug offenders to prevent re-offending. In addition to addressing alcohol and drug misuse and the contribution of alcohol/drugs to violent behavior, such programs might include a greater focus on the routine activities of violent offenders who have alcohol/drug problems [4], focusing especially on identifying and changing the day-to-day activities of these rather marginalized offenders that put them at high risk of interactions that lead to homicide. This could also involve helping offenders to take on more mainstream and less marginalized roles in society. Although the high rate of violent crime associated with certain psychiatric disorders (e.g. personality disorder) has been recognized [5], few studies have examined both substance use/misuse and psychiatric disorders among offenders. While the authors say in the Discussion that these results suggest that a public health approach ‘would place a greater emphasis on substance misuse than on mental health’ (because of the higher rate of alcohol/drug involvement than of psychiatric disorders), the findings also highlight the importance of focusing research and interventions on the combined risk of alcohol/drug misuse and certain psychiatric disorders on future violent offending. This is an important area that has received relatively little attention in previous research. Finally, there are aspects of this paper that could benefit from clarification. The evaluation of the extent that alcohol and drugs contributed to the crime is an important strength of the paper; however, this is a complex topic with which researchers in the field have grappled for some time (see overview by Dingwall [2, chapter 3] and Pernanen & Brochu [6]). The paper could have contributed more to our understanding of this issue by describing their criteria for judging the extent of contribution of alcohol and drugs, the interrater reliability of these judgements and the proportion of offenders who had used these substances at the time but for whom alcohol-drugs were judged as not contributing to their crimes. Information on the specific drugs used at the time and the proportion judged as contributing would also have been extremely useful. Figure 1 describes general drug use among the offenders, but an entirely different drug profile might emerge in terms of drugs contributing to violence.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".