Forensic nursing culture and recovery-oriented practice : a focused ethnography
Bibliographic record
Abstract
Providing Recovery-oriented practice in forensic settings is often a contentious issue. This thesis focused on Recovery with how it is influenced by the forensic population and how it is impacted by forensic nurses’ attitudes and practices. Through a focused ethnography, I explored forensic nursing culture in an inpatient secure setting in Alberta, and the relationship with Recovery-oriented practice when working with the Not Criminally Responsible (NCR) population. Understanding the culture of forensic nursing and how forensic nurses experience and perceive Recovery for forensic patients provided insights into how Recovery can exist and how forensic nurses use Recovery-oriented practices to enhance nursing care in secure settings and facilitate successful reintegration back into community care and society. This research adds to the body of knowledge by demonstrating that there needs to be more Recovery-based training for forensic nurses and how the CHIME Recovery processes should be experienced by forensic nurses in order to move forward with implementation of Recovery-oriented practice, change cultural practices to reflect Recovery instead of rehabilitation, and incorporate a better understanding of Offender Recovery.\n\t\tKeywords: forensic nursing culture, recovery, recovery-oriented practice, offender recovery, secure recovery, not criminally responsible
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".