What is your story? : The experiences of patients and nurses in secure forensic environments
Bibliographic record
Abstract
Nurses who work in forensic environments, practice at the shifting interface of the criminal justice system and the health care system. How they view those in their care, and more importantly, how they engage those in their care, is a significant concern for nursing. Forensic clients are members of a highly stigmatized and stereotyped population. The ability of forensic mental health nurses to provide competent and ethical nursing care is often compromised by personal, social, and political animosity regarding crime, criminality, and mental disorder. Pausing to reflect on the stories of clients and nurses, within a narrative context, evokes understanding, and contributes to the creation of person centered care. In paper one, the coercive treatments experienced by a man who has spent many years in compulsory care in a variety of secure psychiatric settings is explored in response to his confession “I don’t dare to tell them I feel okay!” In paper two, how nurses transition to their roles as forensic nurses is considered as they straddle the custodial and therapeutic aspects of their work, often expressing concerns with their perceptions of “education of the fly” or “faking it ‘til you make it.” In paper three, the mental health contributions of nurses who practice in prisons and correctional institutions is captured in the words “that’s why I bought into this profession, to instill hope and recovery.” Through the examination of these vignettes that have emerged through research and practice, participants will be engaged in an interactive discussion as we consider the implications of narrative nursing vis-à-vis the vast tensions that exist between theory, practice, and research in forensic mental health nursing. Finally, the universal nature of these issues, highlighting contributions from Sweden, Germany and Canada will be illustrated.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.029 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.010 | 0.022 |
| 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".