Development and Validation of a Tool to Improve Physician Identification of Elder Abuse: The Elder Abuse Suspicion Index (EASI)©
Bibliographic record
Abstract
This study aimed to develop and validate a brief tool for physician use to improve suspicion about the presence or absence of elder abuse. A literature review on elder abuse, obstacles to its identification, limitations of detection tools, and characteristics of screeners employed by physicians were used to generate elder abuse detection questions for critique by 31 doctors, nurses, and social workers in focus groups. Six resulting questions became the Elder Abuse Suspicion Index (EASI) administered by 104 family doctors to 953 cognitively intact seniors in ambulatory-care settings. Findings were compared to a recognized, detailed elder abuse Social Work Evaluation (SWE) later administered to participants by social workers blinded to the results of the EASI. The EASI had an estimated sensitivity and specificity of 0.47 and 0.75, usually took less than 2 minutes to ask, and 97.2% of doctors felt it would have some or big practice impact. This research is a first phase in the development and validation of a user-friendly tool that might sensitize physicians to elder abuse and promote referrals of possible victims for in-depth assessment by specialized professionals.
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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 | 0.045 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".