RISE: A CONCEPTUAL MODEL OF INTEGRATED AND RESTORATIVE ELDER ABUSE AND SELF-NEGLECT INTERVENTION
Bibliographic record
Abstract
Abstract Despite a growing number of elder abuse and self-neglect (EASN) cases nationwide, community-based EASN response programs such as Adult Protective Services (APS) lack a conceptually driven, defined, prolonged intervention phase to address these complex situations. This presentation provides a conceptual overview and developmental description of RISE, a community-based model of EASN intervention that works alongside APS or other systems that interact with older adults who are at risk of or experiencing EASN. RISE was developed in consultation with APS caseworkers and supervisors, as well as practitioner and research stakeholders and experts from numerous sectors in Maine and nationwide, building bridges among varying stakeholders. Informed by ecological-systems, relational, and client-centered perspectives and adapting evidence-based or promising modalities from other fields (including motivational interviewing, restorative justice, teaming, supported decision making, goal attainment scaling, and engagement), the RISE model intervenes at levels of the individual older adult victim and others, including the alleged harmer, their relationship, and the systems of informal and formal support surrounding the victim-harmer dyad. The RISE model addresses an intervention gap in response systems to better meet the wishes and needs of EASN victims and others in their lives, leading to more sustainable outcomes. RISE has fostered new research partnerships between researchers and community advocates, who have become both research allies and contributors. RISE supports people with cognitive impairment to make their own decisions. RISE also empowers both older people and younger people in their lives, motivating change, and strengthening and restoring the relationships among them.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".