MétaCan
Menu
Back to cohort
Record W4282978303 · doi:10.1093/geront/gnac083

RISE: A Conceptual Model of Integrated and Restorative Elder Abuse Intervention

2022· article· en· W4282978303 on OpenAlexaff
David Burnes, Marie‐Therese Connolly, Erin Salvo, Patricia Kimball, Geoff Rogers, Stuart Lewis

Bibliographic record

VenueThe Gerontologist · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
FundersAdministration for Community LivingU.S. Department of Health and Human Services
KeywordsIntervention (counseling)Goal Attainment ScalingRestorative justiceDyadMotivational interviewingPerspective (graphical)PsychologyModalitiesConceptual modelEconomic JusticeConceptual frameworkApplied psychologyCriminologySocial psychologyPsychiatryPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

Despite a growing number of elder abuse (EA) cases nationwide, response programs such as adult protective services (APS) lack a defined, prolonged intervention phase to address these complex situations. This article presents RISE, a model of EA intervention that works alongside APS or other systems that interact with at-risk older adults. Informed by an ecological-systems perspective and adapting evidence-based modalities from other fields (including motivational interviewing, teaming, restorative justice, and goal attainment scaling), the RISE model intervenes at levels of the individual older adult victim, individual harmer, their relationship, and community to address EA risk and strengthen systems of support surrounding the victim-harmer dyad. The RISE model addresses an intervention gap in existing systems to better meet the needs of EA victims and others in their lives, leading to more sustainable outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0030.015
Scholarly communication0.0060.007
Open science0.0050.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.069
GPT teacher head0.333
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe GerontologistSame topicElder Abuse and NeglectFrench-language works237,207