Respect in forensic psychiatric nurse-patient relationships: A practical compromise
Bibliographic record
Abstract
The context of forensic psychiatric nursing is distinct from other psychiatric settings as, it involves placement of patients in secure environments with restrictions determined by the courts. Previous literature has identified that nurses morally struggle with respecting patients who have committed heinous offences, which can lead to the patient being depersonalized and dehumanized. Although respect is fundamental to ethical nursing practice, it has not been adequately explored conceptually or empirically. As a result, little knowledge exists that identifies how nurses develop, maintain, and express respect for patients. The purpose of this study is to analyze the concept of respect systematically, from a forensic psychiatric nurse's perspective using the qualitative methodology of focused ethnography. Forensic psychiatric nurses were recruited from two medium secure forensic rehabilitation units. In the first interview, 13 registered nurses (RNs) and two registered practical nurses (RPNs) participated, and although all informants were invited to the second interview, six RNs were lost to follow-up. Despite this loss, saturation was achieved and the data were interpreted through a feminist philosophical lens. Respect was influenced by factors categorized into four themes: (1) emotive-cognitive reactions, (2) nonjudgmental approach, (3) social identity and power, and (4) context. The data from the themes indicate that forensic psychiatric nurses strike a practical compromise, in their understanding and enactment of respect in therapeutic relationships with forensic psychiatric patients.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".