Development and Evaluation of an Elder Abuse Forensic Nurse Examiner e-Learning Curriculum
Bibliographic record
Abstract
In Ontario, Canada, there is a need for an easily accessible training for forensic nurse examiners on the provision of care for abused older adults. In this study, our objective was to develop and evaluate a novel elder abuse nurse examiner e-learning curriculum focused on improving the care provided to older adults. The curriculum was launched on an online learning management system to forensic nurses working across Ontario's hospital-based violence treatment centers in June 2019 and evaluated using pre- and post-training questionnaires that measured self-assessed changes in knowledge and skills-based competence related to providing elder abuse care. There were significant improvements pre- to post-training in self-reported knowledge and competence across all core content domains: Older Adults and Abuse; Documentation, Legal, and Legislative Issues; Interview with Older Adult, Caregiver, and Other Relevant Contacts; Initial Assessment; Medical and Forensic Examination; and Case Summary, Discharge Plan, and Follow-Up Care. As the curriculum enhanced the knowledge and skills associated with caring for abused older adults, it may have implications for training forensic nurse examiners and associated staff working in more than 25 countries internationally.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".