Healthcare in Secure Environments: Leading a Collaborative for Forensic Nursing Education
Bibliographic record
Abstract
Atlantic Canada hosts six federal and 18 provincial correctional facilities distributed across the four provinces. All employ nurses and offer significant career opportunities, yet minimal content related to forensic nursing is provided in nursing curricula. Furthermore, there is a paucity of continuing educational offerings for Canadian forensic nurses. This article describes the series of events that brought the practice of forensic nursing to the forefront of provincial news media. Actions taken by nurses in academia and practice addressed the lack of educational opportunities for forensic nurses in Atlantic Canada. One of these actions, a Knowledge Forum, was held to nurture partnerships between nurse leaders responsible for healthcare in correctional services in New Brunswick and nurse educators. The idea was to connect nurse leaders responsible for healthcare in federal and provincial jurisdictions, community liaison nurses and nurse managers working in hospital-based forensic mental health assessment units, and nurse educators, to explore nursing practice within secure environments and the current educational needs of forensic nurses.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".