Can we distinguish the perpetrators of a homicide-suicide from suicide victims? A systematic review
Bibliographic record
Abstract
The aim of this systematic review is to address the following question: Can we distinguish the perpetrators of homicide-suicide from suicide victims based on the characteristics of the act and its risk factors? The literature search was conducted up until 29 November 2024, using the following databases: PsycInfo, PubMed, Scopus, and ProQuest Dissertations & Theses Global, and resulted in the identification of 24 studies meeting our inclusion criteria. The risk factors that we have identified as more strongly associated with homicide-suicide are male gender, belonging to a minority ethnic group, criminal record, being separated or in the process of separation, a history of violence and partner-related conflicts, involvement in a civil dispute, as well as personality traits or disorders. In contrast, the occurrence of isolated suicide appears to be more associated with being single, living alone, expressing intentions, consulting a healthcare professional, suffering from a physical illness, having a mood disorder, a history of suicide attempts, and issues with drug or alcohol consumption. We reiterate the hypothesis that homicide-suicide and suicide are two distinct outcomes of a common process. The results are interpreted considering the stream analogy for lethal violence. Limitations and future avenues are discussed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".