USE OF MOTIVATIONAL INTERVIEWING IN THE CONTEXT OF ELDER ABUSE INTERVENTION: THE RISE PROJECT
Bibliographic record
Abstract
Abstract Despite the increasing number of elder abuse (EA) cases, many EA victims are reluctant to engage with formal support services, such as Adult Protective Services (APS). For EA interventions to be effective, it is important to overcome this client reluctance. This study examined the use of motivational interviewing (MI) by elder advocates, as a component of a larger EA intervention model, RISE (Repair Harm, Inspire Change, Support Connection, Empower Choice), developed in partnership with Maine APS and the Elder Abuse Institute of Maine. The advocate role was developed, in part, to increase service acceptance/utilization among EA victims. Advocates are trained in MI, a collaborative, client-centered approach designed to help individuals explore and resolve ambivalence around making a change. This study conducted qualitative interviews and a focus group interview with all advocates (n = 4) working within the RISE model to understand how MI is applied in the context of an EA intervention. Three domains were identified: (1) therapeutic relationship, which describes the importance of foundational relationship building to support EA victims; (2) techniques, which refers to the MI strategies that advocates apply and adapt in the context of EA intervention; and (3) implementation challenges, which discusses the difficulties that advocates encounter when using MI with victims of EA. Overall, the experiences of advocates suggest MI is a beneficial and amenable approach to help EA victims navigate feelings of ambivalence and explore their motivation for change. This study represents the first in-depth exploration of MI in the context of EA 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 | 0.039 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".