USING RISE IN CASES DIVERTING DEFENDANTS IN ELDER ABUSE SCENARIOS OUT OF THE CRIMINAL SYSTEM AND INTO TREATMENT
Bibliographic record
Abstract
Abstract Criminal justice system interventions are a common response to elder abuse, neglect and exploitation (EA), but there is severely limited understanding of their efficacy in reducing EA or improving the well-being of older adults. A goal attainment scaling feasibility study revealed that APS clients’ goals often included getting help (like substance use or mental health treatment) for someone else, often family members harming them. Some APS and RISE clients reported reluctance to seek help or report harm, fearing that involvement of the criminal justice system might result in them losing control over their lives, losing a caregiver, being forced into a facility, or legal punishment of someone they care about. Given these findings, the RISE team engaged in a lengthy outreach and planning phase with state and local prosecutors, elder victim service providers, restorative justice experts, and researchers, to design a novel intervention, using RISE in conjunction with drug court, to divert cases involving substance using defendants alleged to have mistreated an older person. This session will describe the extensive planning and design process and provide a case study analysis, which suggests that a combination of drug court’s in-patient treatment for the defendant, services for the victim, and RISE’s focus on restorative justice approaches that repair breached relationships reduced harm and benefited the older person, the defendant, and their families. Implementation of RISE in partnership with the criminal justice system carries important implications for the integration of restorative justice practices in some EA cases.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".