Development of a Comprehensive Hospital-Based Elder Abuse Intervention: An Initial Systematic Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Elder abuse, a universal human rights problem, is associated with many negative consequences. In most jurisdictions, however, there are no comprehensive hospital-based interventions for elder abuse that address the totality of needs of abused older adults: psychological, physical, legal, and social. As the first step towards the development of such an intervention, we undertook a systematic scoping review. OBJECTIVES: Our primary objective was to systematically extract and synthesize actionable and applicable recommendations for components of a multidisciplinary intersectoral hospital-based elder abuse intervention. A secondary objective was to summarize the characteristics of the responses reviewed, including methods of development and validation. METHODS: The grey and scholarly literatures were systematically searched, with two independent reviewers conducting the title, abstract and full text screening. Documents were considered eligible for inclusion if they: 1) addressed a response (e.g., an intervention) to elder abuse, 2) contained recommendations for responding to abused older adults with potential relevance to a multidisciplinary and intersectoral hospital-based elder abuse intervention; and 3) were available in English. ANALYSIS: The extracted recommendations for care were collated, coded, categorized into themes, and further reviewed for relevancy to a comprehensive hospital-based response. Characteristics of the responses were summarized using descriptive statistics. RESULTS: 649 recommendations were extracted from 68 distinct elder abuse responses, 149 of which were deemed relevant and were categorized into 5 themes: Initial contact; Capacity and consent; Interview with older adult, caregiver, collateral contacts, and/or suspected abuser; ASSESSMENT: physical/forensic, mental, psychosocial, and environmental/functional; and care plan. Only 6 responses had been evaluated, suggesting a significant gap between development and implementation of recommendations. DISCUSSION: To address the lack of evidence to support the recommendations extracted in this review, in a future study, a group of experts will formally evaluate each recommendation for its inclusion in a comprehensive hospital-based response.
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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 | 0.099 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".