A SCOPING REVIEW OF OUTCOMES IN ELDER ABUSE INTERVENTION RESEARCH: THE CURRENT LANDSCAPE AND WHERE TO GO NEXT
Bibliographic record
Abstract
Abstract Researchers, practitioners, and policy-makers worldwide recognize elder abuse (EA) as a major threat to the health and well-being of older adults, but rigorous intervention research has greatly lagged behind this interest. A major weakness is the lack of cohesive understanding of appropriate program outcomes to be measured. To address this knowledge gap, we conducted a scoping review of the EA intervention research literature to understand the range of outcomes considered to date and to provide guidance for future research. We searched Ovid MEDLINE, Ovid Embase, Ovid PsycInfo, Ovid Social Work Abstracts, Ebsco AgeLine, Ebsco CINAHL, Wiley Cochrane Central, and Proquest Sociological Abstracts for studies evaluating community-based EA response programs. Two independent reviewers completed record search, screening, and data extraction procedures. We identified 52 eligible studies (1986-2019) that employed a total of 184 outcomes (range: 1–16, mean = 3.5). This study revealed that a large range of outcomes has been employed in EA intervention studies to date, mostly attached to victims or the intervention process itself, with inconsistent operational definitions and measurement procedures. Several key recommendations for future EA intervention research are: 1) implementing intervention outcomes that reflect multiple levels of eco-systemic influence, 2) heightening the analysis of intervention process outcomes beyond description toward identifying factors that mediate or moderate successful case outcomes, 3) conducting qualitative research with EA victims and other relevant stakeholders to understand meaningful intervention outcomes from their perspectives, and 4) establishing common EA outcome measures for implementation across studies to facilitate greater data pooling and synthesis.
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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 | 0.158 | 0.434 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.046 | 0.064 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".