Forensic nursing and multidisciplinary care of the mentally disordered offender
Bibliographic record
Abstract
Preface, David K. Robinson and Alyson M. Kettles. 1. Educational aspects of forensic nursing, Laurence A. Whyte. 2. Overview and contemporary issues in the role of the forensic nurse in the UK, Alyson M. Kettles. 3. The practitioner new to the role of forensic psychiatric nurse in the UK, Mick Collins. 4. A two-nation perspective on issues of practice and provision for professionals caring for mentally disordered offenders, Carol Watson. 5. The role of forensic nurses in the community, Christopher Cordess. 6. A forensic psychiatry perspective, Nigel Hopkins. 8. Transfer of case management from forensic social worker to forensic nurse, Jean Jones, Karen Elliott and Rachael Humpston. 9. Reclaiming the soul: A spiritual perspective on forensic nursing, John Swinton. 10. Autonomy and personhood: The forensic nurse as a moral agent, John Swinton. 11. The role of the forensic nurse in clinical supervision, Mary A. Addo. 12. Staff stress, coping skills and job satisfaction in forensic nursing, Kevin Gournay and Jerome Carson. 13. The role of the forensic psychiatric nurse in the Netherlands, Hans-Martin Don and Tom van Erven. 14. Forensic mentasl health care in Australia, Colin Holmes. 15. The role of the forensic nurse in Canada: An evolving speciality, Cindy Peternelj-Taylor. 16. The role of the forensic nurse in the USA, Anita G. Hufft. 17. The role of forensic nurses in Norway, Roger Almvik, Trond Hatling and Phil Woods. 18. The role of the forensic nurse in Germany, Alison Kuppen and Uwe Donisch-Seidel. 19. A global perspective in forensic nursing: Challenges for the 21st century, Virginia A. Lynch and Zug G. Standing Bear. 20. From a reactive past into the proactive new millennium, Alyson M. Kettles and David K. Robinson. Appendix. References. Index.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".