A New Role of Nursing in Violence: A Reconsideration of Nursing of Victims
Bibliographic record
Abstract
Forensic nursing is the application of forensic science to nursing. It provides direct patient care with relation to violence, abuse, crime, victimization and exploitation. Forensic nurses integrate forensic and nursing sciences in their assessment and care of victims and perpetrators. In the U.S.A., Canada and Europe, forensic nursing practice involves advocating for the collection of evidence and reporting of crimes. Additionally, forensic nurses treat victims and perpetrators for their trauma, their families, communities and the systems that respond to them. Through its practice, forensic nursing contributes to public health by preventing health hazards caused by violence and crime. This report considers the possibility of the development of forensic nursing in Japan. We propose that the development of forensic nursing is necessary.
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".