Forensic nursing educational development: an integrated review of the literature
Bibliographic record
Abstract
Accessible summary The importance of this paper's findings are: Forensic nursing is a nursing specialty with subspecialties that focus on nursing practice who care for victims and offenders, living and deceased at the clinical legal interface. The gaps identified in the literature indicate where educational development did not occur concurrently with role development of forensic nursing. Role development occurred ahead of educational development for all subspecialty areas of forensic nursing except sexual assault nurse examiners, where it developed simultaneously. Forensic nursing formal education did not develop sooner as infrastructures were not in place to support it. Abstract The forensic area of practice has been a popular career choice and area of study for many of the health science disciplines. Forensic nursing is a nursing specialty with subspecialties that focus on nursing practice who care for victims and offenders, living and deceased at the clinical legal interface. This integrated review of the literature overviewed the historical development of each of the forensic nursing subspecialties and identified gaps in specialty nursing educational development. Although multiple studies for the last 30 years identified the need for forensic nursing education, recommendations did not soon translate into educational curriculum development. The literature showed that role development was not concurrent with educational development in all forensic nursing subspecialties.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".