Effect of an elder abuse and self‐neglect intervention on repeat investigations by adult protective services: <scp>RISE</scp> project
Bibliographic record
Abstract
BACKGROUND: Adult Protective Services (APS) is the primary agency responsible for investigating elder abuse and self-neglect (EASN) allegations in the United States. The harms of EASN are well established; however, APS lacks a conceptually derived evidenced-based intervention phase. RISE is a community-based intervention designed to complement APS that provides enhanced services and a longer intervention phase. The objective of this study was to test whether exposure to the RISE/APS collaboration was associated with reducing the case outcome of recurrence (repeat investigations) compared to usual care APS only services. METHODS: A retrospective observational study (n = 1947) of two counties in Maine where RISE was available to provide enhanced services to persons referred from APS. An extended regression endogenous treatment Probit model using APS administrative data was used to predict case recurrence. RESULTS: Between July 2019 and October 2021, 154 cases participated in RISE and 1793 received usual APS only services. 49% of cases in RISE had 2 or more prior substantiated allegations versus 6% for those receiving usual APS care, and 46% of cases in RISE had a recurrence during the observation period versus 6% for usual care group. However, after accounting for the non-random treatment assignment, RISE was associated with a significantly lowered 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). CONCLUSIONS: A reduction in recurrence carries important implications for APS clients, costs, resources, and workflow. It may also serve as a proxy indicating a reduction in revictimization and harm for EASN victims.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".