Nurse‐Administered Screening Tools for Detecting Elder Abuse in Emergency Departments: A Scoping Review
Bibliographic record
Abstract
AIM: Identify and describe nurse-administered screening tools used in emergency departments (ED) to detect elder abuse. DESIGN: A scoping review of literature published between 1999 and 2024 was conducted following the guidance of the Joanna Briggs Institute Manual for Evidence Synthesis and a methodological framework for scoping studies. METHODS: Two reviewers, an academic faculty member and a senior undergraduate, conducted the screening and data extraction, aiming to identify studies using a nurse-administered screening tool in the ED to detect elder abuse. DATA SOURCES: The final search was conducted on 24 April 2024, using the CINAHL, MEDLINE, PsycINFO, and Cochrane Review databases. RESULTS: Ten studies out of 145 met the inclusion criteria, identifying six screening tools that assist healthcare providers, such as nurses, in detecting elder abuse in EDs. The results were summarised and presented according to each screening tool. IMPLICATIONS FOR THE PROFESSION: Nurses in EDs are well-positioned to identify elder abuse due to the significant time spent observing and interacting with patients. The implementation of a screening tool can support nurses in detecting elder abuse and initiating appropriate interventions. IMPACT: Elder abuse is a widespread public health issue projected to increase continuously with the rapidly ageing population. Incorporating nurse-administered screening tool into EDs has demonstrated practicality and usefulness in identifying elder abuse cases. Various tools exist; however, these instruments are underutilised due to limited reliability and feasibility testing, with no definitive screening tool identified as the "gold standard" for elder abuse detection. Without formal screening, elder abuse is likely to remain undetected, leaving victims vulnerable to harmful consequences. Due to the limited testing and evaluation of a reliable ED screening tool for elder abuse, future research should focus on developing and validating a new screening tool intended specifically for use by nurses in EDs. REPORTING METHOD: The EQUATOR guidelines for PRISMA were met. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contributions.
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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 | 0.033 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".