The forensic float nurse: A new concept in the effective management of service delivery in a forensic program
Bibliographic record
Abstract
A major challenge faced by Forensic Program management teams is to balance their budgets due to the unpredictability of the forensic patient population, particularly in the context of managing staffing costs where the hospital is not the "gatekeeper" and does not have control over who is admitted and when. In forensic mental health, the justice system, either via the courts, or review boards, determines who is ordered for admission to hospital for assessment or treatment and rehabilitation. Hospitals have little, if any, recourse but to admit these mentally disordered offenders. This typically results in increased levels of staffing with concomitant overtime costs. The literature suggests that clustered float pool nurses develop enhanced relationships with staff and patients, thereby enabling them to attain specialized clinical expertise to treat specific patient populations, promoting safer, high quality care, and overall are more cost effective. Forensic nursing is recognized as a mental health subspecialty. The "Forensic Float Nurse" concept was piloted to provide readily available, highly adaptable, skilled forensic nurses to assist in times of unpredictably heavy workloads and/or unplanned staffing shortages. A significant reduction approaching 50% in overtime was achieved. Heuristic implications of this finding are presented.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".