Community Elder Mistreatment Intervention With Capable Older Adults: Toward a Conceptual Practice Model
Bibliographic record
Abstract
Community-based elder mistreatment response programs (EMRP), such as adult protective services, that are responsible for directly addressing elder abuse and neglect are under increasing pressure with greater reporting/referrals nationwide. Our knowledge and understanding of effective response interventions represents a major gap in the EM literature. At the center of this gap is a lack of theory or conceptual models to help guide EMRP research and practice. This article develops a conceptual practice model for community-based EMRPs that work directly with cognitively intact EM victims. Anchored by core EMRP values of voluntariness, self-determination, and least restrictive path, the practice model is guided by an overarching postmodern, constructivist, eco-systemic practice paradigm that accepts multiple, individually constructed mistreatment realities and solutions. Harm-reduction, client-centered, and multidisciplinary practice models are described toward a common EMRP goal to reduce the risk of continued mistreatment. Finally, the model focuses on client-practitioner relationship-oriented practice skills such as engagement and therapeutic alliance to elicit individual mistreatment realities and client-centered solutions. The practice model helps fill a conceptual gap in the EM intervention literature and carries implications for EMRP training, research, and practice.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".