Developing a screening tool and intervention strategy for elder neglect in persons with dementia in primary care: protocol to use a multistep process
Bibliographic record
Abstract
INTRODUCTION: Elder neglect by both informal and formal caregivers is common, particularly among persons with dementia, and has serious health consequences but is under-recognised and under-reported. Persons with dementia are often unable to report neglect due to memory and language impairments, increasing their vulnerability. Screening for elder mistreatment and initiation of intervention in primary care clinics may be helpful, but few evidence-based tools or strategies exist. We plan to: (1) develop a novel primary care screening tool to identify elder neglect in persons with dementia, (2) develop an innovative technology-driven intervention for caregivers and (3) pilot both for feasibility and acceptability in primary care. METHODS AND ANALYSIS: We will use a multistep process to develop a screening tool, including a modified Delphi approach with experts, and multivariable analysis comparing confirmed cases of neglect in patients with dementia from the existing data registry to non-neglected controls. We will develop an evidence-based, technology-driven caregiving intervention for neglect with an expert panel and iterative beta testing. Following the development of the protocol for implementation of the tool and intervention with associated training, we will pilot test both the tool and intervention in older adult patients and caregivers. We will conduct provider focus groups and interviews with patients and caregivers to assess usability and will modify the tool and intervention. These studies are in preparation for a future randomised trial. ETHICS AND DISSEMINATION: Initial phases of this project have been reviewed and approved by the Weill Cornell Medicine Institutional Review Board, protocol #22-06024967, with initial approval on 1 July 2022. We aim to disseminate our results in peer-reviewed journals, at national and international conferences and among interested patient groups and the public.
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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 | 0.072 | 0.070 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".