Exploring Risk of Elder Abuse Revictimization: Development of a Model to Inform Community Response Interventions
Bibliographic record
Abstract
A focus of community-based elder abuse response programs (EARP), such as Adult Protective Services, is to reduce the risk of revictimization among substantiated victims. While elder abuse (EA) risk factor research has predominantly focused on understanding the risk of initial EA onset among the general older adult population, understanding of revictimization risk among substantiated victims is weak. This study sought to identify conditions that perpetuate EA among substantiated victims. Data were collected from multiple sources: focus groups with multidisciplinary teams ( n = 35), multidisciplinary team case revictimization risk evaluations ( n = 10), and reviewing a random sample of case records ( n = 250) from a large EARP in New York City. Sixty-two indicators of EA revictimization risk were identified across several ecosystemic levels: individual victim or perpetrator, victim–perpetrator relationship, and surrounding family, home, community, and sociocultural contexts. Findings carry implications for EARP practices to reduce EA recurrence and the development of measures to evaluate EARP intervention.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".