Older Adult Homicide: Investigating Case, Victim and Perpetrator Characteristics in a National Sample from England and Wales
Bibliographic record
Abstract
Abstract Older adult homicide (OAH) is the most severe, yet understudied, form of older adult abuse. This study examined the case, victim and perpetrator characteristics of OAH. A secondary analysis of national data from England and Wales (2008–2019) was conducted where cases of non-stranger OAH (victims aged sixty years and over) were compared to adult homicide (victims aged eighteen to fifty-nine years) at the case, victim (n = 3,274) and perpetrator (n = 2,763) levels. Logistic regression models used to identify characteristics that were OAH risk factors, showed only a slight increase in predictive power but high accuracy in classifying adult homicide cases. Nevertheless, some risk factors known to be predictors of older adult abuse were significant predictors of OAH (e.g. living with the perpetrator, the perpetrator’s mental state). Implications for research, policy and practice are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".