USING A “TEAMING” (ENHANCED SOCIAL SUPPORT) APPROACH IN THE CONTEXT OF THE RISE ELDER ABUSE AND SELF-NEGLECT INTERVENTION
Bibliographic record
Abstract
Abstract 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. However, data consistently demonstrate that social support is a protective factor. As a component of a larger community-based EASN intervention, RISE, this study examined the use of a method called “teaming,” a wraparound approach to establish sustained formal and informal social supports surrounding victims and alleged harmers in EASN cases. Qualitative interviews and a focus group were conducted with the original pilot cohort of RISE “advocate” caseworkers (n = 4). A descriptive phenomenological approach involving two independent assessors was used to code transcripts into themes. 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 utilized to promote collectivity and shared responsibility around the client; and (3) implementation challenges, which discusses the difficulties advocates encountered when using teaming with people experiencing EASN. 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. This study represents the first in-depth exploration of teaming in the context of EASN intervention.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".