Familicide: A Systematic Literature Review
Bibliographic record
Abstract
Familicides have received relatively little attention and are mostly discussed in studies with broader aims. Here, we reviewed 67 studies from 18 countries on familicides, in which an offender killed or attempted to kill their current or former spouse/intimate partner and one or more of their biological or stepchildren. We conducted a systematic literature search in PubMed, PsycINFO, and Google Scholar. Eight studies investigated familicide specifically, while the remaining reported on familicide cases as a subsample. We retrieved data on offenders' gender, age, and background as well as on victims and their relationship to the offender. We also retrieved data on contextual factors and offense characteristics (i.e., modus operandi, offense location, premeditation, and whether or not the offender had committed suicide). We also coded methodological aspects of the studies. Familicides were almost exclusively committed by men and about half of the familicide cases led to the suicide of the offender. Mental health problems, relationship problems, and financial difficulties were prevalent. Because few studies reported population base rates of the investigated characteristics, it is difficult to draw conclusions about specific risk factors. Future research should further investigate typologies of familicide and examine risk factors for different types of familicides.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".