Older Perpetrators of Domestic Violence: Mixed-Effects Logistic Regression Analysis of Police Records
Bibliographic record
Abstract
BACKGROUND: Domestic violence (DV) among older adults is an understudied area, often overlapping with abuse of older people, intimate partner violence, and behavioral and psychological symptoms of dementia. OBJECTIVE: This study aimed to examine the characteristics of older persons of interest-individuals suspected or charged with a DV offence-and survivors involved in police-attended DV events in New South Wales, Australia, and assess associations with physical and nonphysical abuse. METHODS: Police records of 10,708 DV events involving 8247 adults aged ≥55 years who were identified as persons of interest from 2005 to 2016 were analyzed using text mining. A 3-level mixed-effects logistic regression model was used to identify predictors of physical and nonphysical abuse. RESULTS: Physical abuse formed a greater proportion of all abuse committed by female persons of interest aged >65 years compared to female persons of interest aged between 55 and 64 years and male persons of interest; however, after stratified analysis, female persons of interest had similarly elevated odds of physical abuse perpetration to male persons of interest. Other factors associated with increased odds of perpetrating physical abuse were persons of interest with dementia and alcohol-related events. Dementia increased the odds of combined physical and nonphysical abuse. Substance use disorders increased the odds of events with combined physical and nonphysical abuse. CONCLUSIONS: The findings of this study suggest that DV, including physical violence, is an important issue in later life. Alcohol is a situational factor, and dementia is associated with perpetration and exposure to violence. The study highlights the need for clinicians to evaluate the risk of violence and exposure to violence in patients with dementia and for policy interventions targeting alcohol and substance use in older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".