Nurses' Experiences of Using Coercion in Forensic and Non‐Forensic Settings: A Constant Comparative Analysis
Bibliographic record
Abstract
INTRODUCTION: Coercive measures are increasingly used in psychiatric settings, especially in forensic settings. Coercive measures such as seclusion, restraints and involuntary care cause negative outcomes for both people living with mental illness and nurses. AIM: The aim of this paper is to compare the perspectives of nurses who experience the use of coercive measures in forensic and general psychiatric care. METHOD: Grounded theory was used as a qualitative methodology. We used the constant comparative method to analyse the data. Individual interviews were conducted with nurses from general psychiatry (n = 9) and forensic psychiatry (n = 9). RESULTS: Four categories were determined: (1) Towards a contextual understanding of coercion; (2) Justifications for the use of coercion; (3) Maintaining a relationship of trust; and (4) Influence of the culture of control. DISCUSSION: Nurses providing care in a coercive context-whether in general psychiatric or forensic settings-face important ethical dilemmas. Several factors can influence the application of coercion, including a paternalistic culture of risk management. IMPLICATIONS FOR PRACTICE: A considerate and empathetic approach, grounded in a posture of advocacy, helps to prevent the use of coercion. RELEVANCE STATEMENT: This paper may raise awareness among mental health nurses working with patients who are involved in the justice system. Psychiatric nurses are particularly affected by the application of coercion in their clinical practice. The theoretical framework used in this article is well suited to an exploration of the dual roles imposed on psychiatric nurses (care and control). Last, this paper highlights the need to stimulate discussion and critical reflection among nurses regarding the duality of control and care and the ongoing application of coercion in clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".