SCREENING AND ASSESSMENT OF ELDER ABUSE: AN EVALUATION OF NICE TOOLS
Bibliographic record
Abstract
Elder abuse (EA) has various dimensions, including physical, sexual and psychological abuse, which make screening and assessment challenging. As part of a larger project, the National Initiative for the Care of the Elderly (NICE) developed evidence-based tools to address these challenges. However, as no formal evaluation of these instruments has been conducted, the current study examines and evaluates the impact of NICE EA tools. Participants with NICE membership were randomly sampled (n = 438: 79.7% practitioners; 7.5% students; 4.5% older adults/informal caregivers; and 8.2% other) and asked to complete a telephone survey to assess the instrumental impact (use of tools), conceptual impact (impact of knowledge in tools), and symbolic impact (whether the tools confirmed actions/decisions) of the tools. Of 438 participants, 74 reported using EA tools the most, with 46% of these users indicating that the tools had an instrumental impact (i.e., information in the EA tool changed their daily work practices and/or they adopted ideas/actions from the tool). Additionally, 31% indicated that the tools had a conceptual impact, as the tools increased their knowledge of EA and influenced their work practices. Finally, 45% reported the EA tools confirmed their actions at work and helped justify their decisions to co-workers and clients. These results suggest that NICE EA pocket tools have a conceptual, instrumental, and symbolic impact on improving knowledge and practices related to EA among multiple stakeholders. Screening tools, such as the ones developed by NICE, may help raise awareness to the possibility of elder abuse.
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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 | 0.055 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".