Preliminary findings on the use of “teaming” in elder abuse intervention: the RISE project
Bibliographic record
Abstract
Purpose Our understanding of what intervention strategies are effective in improving the well-being of older adults experiencing elder abuse and self-neglect (EASN) is severely limited. The purpose of this study was to examine the use of a method called “teaming,” a wraparound approach to provide enhanced social support to older adults experiencing EASN. A teaming intervention was administered by advocates in Maine, USA, as a component of a larger community-based EASN intervention, Repair harm, Inspire change, Support connection, Empower choice (RISE), implemented to complement adult protective services. Design/methodology/approach Qualitative interviews and a focus group were conducted with RISE advocates (n = 4). A descriptive phenomenological approach involving two independent assessors was used to code transcripts into themes and subthemes. Findings Three domains were identified: (1) team and support forming process, which describes the development of a supportive network based on each client’s needs; (2) techniques, which refers to the specific strategies advocates use to promote collectivity and shared responsibility around the client; and (3) implementation challenges, which discusses the difficulties advocates encounter when using teaming with people experiencing EASN. Originality/value This study represents the first in-depth exploration of teaming in the context of EASN intervention. Preliminary findings on the experiences of advocates suggest that teaming is a beneficial approach to support the individualized needs of each client, and to promote improved and sustainable case outcomes for clients.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".