Piloting the Elder Abuse Suspicion Index – long‐term care (EASI‐ltc©): A Mixed Methods Feasibility Study
Bibliographic record
Abstract
Abstract Background Long‐term care (LTC) residents are a previously untested and highly vulnerable population at risk of elder abuse (EA) and its many negative health outcomes. The detection of elder abuse within the LTC context is urgent and time‐sensitive. Objective The overarching aim of this study is to evaluate the feasibility of implementing the Elder Abuse Suspicion Index – long‐term care (EASI‐ltc©): the first comprehensive detection tool of its kind designed specifically to identify the abuse of cognitively‐apt persons living in LTC. Preliminary tool validity will also be evaluated. Methods This observational pilot study is taking place within seven LTC institutions in Montreal Canada. It begins with the administration of the EASI‐ltc on a random sample of eligible LTC residents. Residents subsequently undergo a follow‐up assessment led by a trained and experienced social worker, which is used as the ‘silver standard’ reference for validation. Residents are asked to reflect on their own experiences as participants in one on‐one interviews, and key stakeholders (e.g., administrators, staff members, family, companions, and friends) are asked to provide retroactive feedback via an online survey. Survey respondents are also invited to participate in interviews to clarify their responses. Potential harmful consequences arising from EASI‐ltc administration is being evaluated. Results Results pertaining to tool validity, feasibility, and acceptability obtained from data collected during the first 8 months will be presented. Conclusion The EASI‐ltc is the first published comprehensive tool to detect elder abuse in this vulnerable population. This ongoing study responds to an urgent need to provide tools to identify abuse and give a voice to LTC residents locally, nationally, and beyond.
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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 | 0.056 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".