Adapting the Elder Abuse Suspicion Index© for use in the geriatric long-term care setting
Bibliographic record
Abstract
Background: Elderly individuals in long-term care facilities (LTCFs) are at high risk for elder abuse (EA) and its related health consequences. However, currently available EA screening and identification tools have limitations for use in this population. Objectives: 1) To explore whether an existing EA detection tool, the Elder Abuse Suspicion Index© (EASI), is appropriate for use with residents with Mini-Mental State Examination (MMSE) scores ≥ 24 residing in LTCFs; 2) to adapt the content of the EASI (if required) to fit this new population; and 3) to explore contextual factors that may affect use of the resulting tool in LTCFs. Methods: This was a mixed methods study sequentially integrating quantitative cross-sectional and qualitative descriptive methodologies. Results were informed by a literature review, internet-based consultations with EA experts across Canada, and data obtained from two purposively selected focus groups. Efforts were made to specifically distinguish between institutional or systems failure, and resident-directed abuse. Results: Analyses resulted in the development of a nine-question tool, the EASI-ltc, designed to raise suspicion of EA in older adults with MMSE scores ≥ 24 residing in LTCFs. Notable modifications to the original EASI included three new questions to further address neglect and psychological abuse, and a context-specific preamble to orient responders. Resident reluctance to report abuse and a lack of defined reporting protocols/procedures were identified as potential barriers to successful EASI-ltc implementation. Conclusions: It is expected that the EASI-ltc will advance understanding of abuse experienced by LTC residents. As any indication of suspicion raised as a result of the tool necessitates further abuse evaluation and institutional response, future validation of the EASI-ltc may lead to reliable EA prevalence data in this population. The next steps in this multi-phase research program will be to develop a research protocol to explore the practical aspects of EASI-ltc implementation, and to conduct a feasibility pilot study.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".