Adapting the Elder Abuse Suspicion Index© for Use in Long-Term Care: A Mixed-Methods Approach
Bibliographic record
Abstract
Currently available elder abuse screening and identification tools have limitations for use in long-term care (LTC). This mixed-methods study sought to explore the appropriateness of using the Elder Abuse Suspicion Index© (a suspicion tool originally created for use with older adults in the ambulatory setting with Mini-Mental State Examination scores ≥ 24) with similarly cognitively functioning persons residing in LTC. Results were informed by a literature review, Internet-based consultations with elder abuse experts across Canada ( n = 19), and data obtained from two purposively selected focus groups ( n = 7 local elder abuse experts; n = 7 experienced front-line LTC clinicians). Analyses resulted in the development of a nine-question tool, the EASI-ltc, designed to raise suspicion of EA in cognitively intact older adults residing in LTC (with little or no cognitive impairment). Notable modifications to the original Elder Abuse Suspicion Index© (EASI) included three new questions to further address neglect and psychological abuse, and a context-specific preamble to orient responders.
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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 | 0.002 | 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".