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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".