Adapting RISE Into Montana Adult Protective Services: An Embedded Model
Bibliographic record
Abstract
Abstract Understanding effective elder abuse and self-neglect (EASN) interventions remains limited. Adult Protective Services (APS) is primarily responsible for investigating EASN reports in the U.S. However, APS generally lacks a dedicated intervention phase to address underlying case needs/risks. RISE is an evidence-based EASN intervention that works in partnership with APS to address this intervention gap. RISE integrates core modalities (motivational interviewing, restorative approaches, teaming, engagement, goal attainment scaling) and operates at Relational, Individual, Social, and Environmental levels to work with older adult victims and alleged harmers and strengthen social supports surrounding them. To date, the RISE-APS model has involved complementary partnerships where RISE operates from external community-based organizations. In partnership with Montana APS, this study sought to understand how to adapt and implement a RISE-APS “embedded” model, where RISE operates from within Montana APS as a specialized unit providing extended services for complex cases. This RISE-APS adaptation/implementation project was guided by the Consolidated Framework for Implementation Research (CFIR), containing factors across five domains (intervention, outer setting, inner setting, individuals, implementation process) representing potential internal/external implementation barriers/facilitators. Informed by CFIR and using a descriptive phenomenological qualitative approach, we conducted focus groups and individual interviews with Montana APS management (n = 4) and social service workers (5) as part of an adaptation needs assessment to identify implementation barriers/facilitators and engaged in a process to determine strategies to address/leverage them. This presentation will present an adaptation process model for the embedded RISE-APS model, identifying implementation determinants (barriers, facilitators), actions (strategies, mechanisms), and outcomes (adoption, fidelity, sustainment).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".