Development of skills-based competencies for forensic nurse examiners providing elder abuse care
Bibliographic record
Abstract
OBJECTIVE: As a critical step in advancing a comprehensive response to elder abuse built on existing forensic nursing-led hospital-based programmes, we developed a list of skills-based competencies for use in an Elder Abuse Nurse Examiner curriculum. PARTICIPANTS AND SETTING: Programme leaders of 30 hospital-based forensic nursing-led sexual assault and domestic violence treatment centres. PRIMARY AND SECONDARY OUTCOME MEASURES: 149 verbatim recommendations for components of an elder abuse response were identified from a systematic scoping review. In 2 online Delphi consensus survey rounds, these components of care were evaluated by an expert panel for their overall importance to the elder abuse intervention under development and for their appropriateness to the scope of practice of an elder abuse nurse examiner. The components retained after evaluation were translated into skills-based competencies using Bloom's Taxonomy of Learning and, using the Nominal Group Technique, were subsequently reviewed and revised by a subset of members of the expert panel in a consensus meeting. RESULTS: Of the 148 recommendations evaluated, 119 were rated as important and achieved consensus or high level of agreement. Of these, 101 were determined to be within the scope of practice of an Elder Abuse Nurse Examiner and were translated into skills-based competencies. Following review and revision by meeting experts, 47 final competencies were organised by content into 5 metacompetencies: documentation, legal and legislative issues; interview with older adult, caregiver and other relevant contacts; assessment; medical and forensic examination; and case summary, discharge plan and follow-up care. CONCLUSIONS: We determined the skills-based competencies of importance to training forensic nurse examiners to respond to elder abuse in the context of a hospital-based intervention. These findings may have implications for violence and abuse treatment programmes with a forensic nursing component that are considering the provision of a dedicated response to the abuse of older women and men.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".