Working under threat: Fear and nurse–patient interactions in a forensic psychiatric setting
Bibliographic record
Abstract
The purpose of this article is to present the results of a study conducted in a Canadian medium-security forensic psychiatric facility. The primary objective of this qualitative research was to describe and comprehend how fear influences nurse-patient interactions in a forensic psychiatric setting. Eighteen semistructured interviews with nurses were used as the primary source of data for analysis. In brief, the results from this research indicate, as other researchers have demonstrated, that within this highly regimented context, nurses are socialized to incorporate representations of the patients as being potentially dangerous, and, as a result, distance themselves from idealistic conceptions of care. Moreover, the research results emphasize the implication of fear in nurse-patient interactions and particularly how fear reinforces nurses' need to create a safe environment in order to practice. A constant negotiation between space, "at risk" bodies and security takes place where nurses are forced to scrutinize their actions in order to avoid becoming victims of violence. In parallel, participants also described how being able to self-identify with patients enabled therapeutic interventions to take place. However, exposure to the patient's criminal history fostered negative reactions on the nurses' part, which impede nursing work.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".