The Emergence of Forensic Nursing and Advanced Nursing Practice in Switzerland
Bibliographic record
Abstract
BACKGROUND AND METHODS: The objectives of this article were to systematically describe and examine the novel roles and responsibilities assumed by nurses in a forensic consultation for victims of violence at a University Hospital in French-speaking Switzerland. Utilizing a case study methodology, information was collected from two main sources: (a) discussion groups with nurses and forensic pathologists and (b) a review of procedures and protocols. Following a critical content analysis, the roles and responsibilities of the forensic nurses were described and compared with the seven core competencies of advanced nursing practice as outlined by Hamric, Spross, and Hanson (2009). RESULTS: Advanced nursing practice competencies noted in the analysis included "direct clinical practice," "coaching and guidance," and "collaboration." The role of the nurse in terms of "consultation," "leadership," "ethics," and "research" was less evident in the analysis. DISCUSSION AND CONCLUSION: New forms of nursing are indeed practiced in the forensic clinical setting, and our findings suggest that nursing practice in this domain is following the footprints of an advanced nursing practice model. Further reflections are required to determine whether the role of the forensic nurse in Switzerland should be developed as a clinical nurse specialist or that of a nurse practitioner.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".