RISE: A COMMUNITY-BASED ELDER ABUSE AND SELF-NEGLECT RESPONSE INTERVENTION ADDRESSING A CRITICAL SYSTEMS GAP
Bibliographic record
Abstract
Abstract Knowledge of effective interventions for elder abuse and self-neglect (EASN) is limited. Adult Protective Services (APS) represents the primary agency responsible for receiving reports and investigating suspected cases of EASN in the US. However, APS lacks a distinct, conceptually informed intervention phase to support EASN cases. Based on theory, research, and consultations with stakeholders, RISE was designed to address this intervention gap within overall EASN response systems. Informed by ecological-systems, relational, and client-centered perspectives, RISE is a community-based EASN intervention that integrates core modalities (motivational interviewing, restorative justice, teaming, supported decision making) demonstrating evidence and/or promising results in EASN and other domains. The intervention operates at Relational, Individual, Social, and Environmental levels of ecological influence. Specifically, RISE works with both older adult victims and others, including alleged harmers, their relationships, and strengthens the social supports surrounding them. RISE began as a pilot in two Maine counties, was expanded to the entire state, has been used in over 450 cases, was written into Maine’s 2023 budget, is now being implemented and tested in New Hampshire and Toronto, Canada, and is being expanded to the criminal justice system. This symposium will describe RISE’s development and conceptual underpinnings (presentation 1), findings on implementing “teaming” (social support), an intervention modality (presentation 2), a qualitative evaluation of RISE from the perspective of APS caseworkers (presentation 3), evidence of RISE reducing EASN recidivism (presentation 4), and case studies of implementing RISE (and its restorative justice modality) in the criminal justice diversion context (presentation 5). This is an Abuse, Neglect and Exploitation of Older Persons Interest Group Sponsored Symposium.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".