EFFECT OF RISE ON REPEAT ELDER ABUSE AND SELF-NEGLECT INVESTIGATIONS BY ADULT PROTECTIVE SERVICES
Bibliographic record
Abstract
Abstract Adult Protective Services (APS) is the primary agency responsible for investigating Elder Abuse and Self-Neglect (EASN) allegations in the US. The harms of EASN are well established; however, APS lacks a conceptually derived, evidenced-based intervention phase. RISE is a community-based EASN intervention designed to complement and augment APS that provides enhanced services and a longer intervention phase. The objective of this study was to test whether exposure to the APS/RISE collaboration reduced the case outcome of recurrence (repeat investigations) compared to usual APS only services. This study was based on a retrospective observational design (n = 1947) in two randomly selected counties of Maine where RISE was available to provide enhanced services to persons referred from APS. Analysis used an extended regression endogenous treatment Probit model using APS administrative data to predict case recurrence. Between 2019 and 2021, 154 cases participated in RISE and 1793 received usual APS only services. 49% of cases referred to RISE had a complex history with 2 or more prior substantiated allegations versus 6% for those receiving usual APS only services. After accounting for the non-random treatment assignment, RISE significantly lowered the likelihood of recurrence compared to persons receiving usual care provided by APS (probability of recurrence reduced by 0.55 for the Average Treatment Effect on the Treated and 0.26 for the Average Treatment Effect). A reduction in recurrence carries important implications for APS costs, resources, and workflow. It may also serve as a proxy indicating a reduction in re-victimization and harm for EASN victims.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".