Elder Abuse Detection and Intervention: Challenges for Professionals and Strategies for Engagement From a Canadian Specialist Service
Bibliographic record
Abstract
Elder abuse (EA) is of increasing relevance in the context of an aging society, and this has implications for detection and intervention for several types of healthcare providers, including forensic nurses. Knowledge related to EA is important as victims are likely to interact with providers, because of either existing health problems or the consequences of abuse. This article provides a brief overview of EA, followed by an outline of current detection and intervention efforts used by healthcare providers in community and hospital settings. In addition, knowledge about help-seeking and barriers to disclosure are discussed to inform healthcare provider interactions with older adults where EA is suspected or disclosed. To illustrate challenges faced by healthcare providers in this area, two cases of EA involving case management by a forensic nurse in a specialist service in Canada are presented.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".