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Record W4390082268 · doi:10.1093/geroni/igad104.1155

A QUALITATIVE EVALUATION OF RISE FROM THE PERSPECTIVE OF ADULT PROTECTIVE SERVICES CASEWORKERS

2023· article· en· W4390082268 on OpenAlexaff
Geoff Rogers, Andie MacNeil, Martin Connolly, Erin Salvo, Patricia Kimball, Stuart Lewis, David Burnes

Bibliographic record

VenueInnovation in Aging · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeneral partnershipPsychological interventionIntervention (counseling)Perspective (graphical)Agency (philosophy)ReferralScope (computer science)NeglectPublic relationsPsychologyQualitative researchMedicineBusinessPolitical scienceNursingSociologyComputer science

Abstract

fetched live from OpenAlex

Abstract Our understanding of effective elder abuse and self-neglect (EASN) response interventions is limited. Adult Protective Services (APS), the primary agency responsible for responding to EASN, lacks a coherent, conceptually driven, prolonged intervention phase. Informed by ecological-systems, client-centered, and relational perspectives and adapting evidence-based modalities from other fields, the RISE intervention addresses this systems gap and compliments and augments APS services. Based on a three-year pilot project involving a partnership between RISE and Maine APS, the current study conducted a qualitative evaluation of RISE, from the perspective of APS caseworkers (n=14) who worked with RISE. The purpose of this evaluation was to understand RISE strengths, areas for improvement, and qualities of the RISE/APS partnership. Findings suggest APS workers perceive that RISE benefits clients, complements the scope and nature of APS, enhances APS caseworker well-being, and reduces repeat APS cases. Further APS/RISE collaboration and clarification on RISE role responsibilities and referral eligibilities represent areas of growth. This study provides preliminary evidence for RISE as a community-based EASN intervention in partnership with APS

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.448
Teacher spread0.359 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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