Homicide-suicides compared to homicides and suicides: systematic review and meta-analysis.
Bibliographic record
Abstract
Homicide-suicides, the murder of one or several individuals followed by the suicide of the perpetrator, are rare but have devastating effects on families and communities. We did a systematic review and meta-analysis of studies comparing perpetrators of homicide-suicides with perpetrators of simple homicides and suicides and examined the proportion of firearm use and its correlation with firearm availability. We searched Medline and Embase from inception to July 2012 and identified 27 eligible studies. Perpetrators of homicide-suicides were older and more likely to be male and married to or separated from their victims than perpetrator of simple homicides or suicides. Influence of alcohol and a history of domestic violence or unemployment were less prevalent in homicide-suicides than in homicides. The proportion of firearm use in homicide-suicides varied across countries and was highest in the USA, Switzerland and South Africa, followed by Australia, Canada, The Netherlands and England and Wales, with a strong correlation between the use of firearms and the level of civilian gun ownership in the country. Our results indicate that homicide-suicides represent a distinct entity, with characteristics distinguishing them both from homicides and suicides.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".