Safety in psychiatric inpatient care: The impact of risk management culture on mental health nursing practice
Bibliographic record
Abstract
The discourse of safety has informed the care of individuals with mental illness through institutionalization and into modern psychiatric nursing practices. Confinement arose from safety: out of both societal stigma and fear for public safety, as well as benevolently paternalistic aims to protect individuals from self-harm. In this paper, we argue that within current psychiatric inpatient environments, safety is maintained as the predominant value, and risk management is the cornerstone of nursing care. Practices that accord with this value are legitimized and perpetuated through the safety discourse, despite evidence refuting their efficacy, and patient perspectives demonstrating harm. To illustrate this growing concern in mental health nursing care, we provide four exemplars of risk management strategies utilized in psychiatric inpatient settings: close observations, seclusion, door locking and defensive nursing practice. The use of these strategies demonstrates the necessity to shift perspectives on safety and risk in nursing care. We suggest that to re-centre meaningful support and treatment of clients, nurses should provide individualized, flexible care that incorporates safety measures while also fundamentally re-evaluating the risk management culture that gives rise to and legitimizes harmful practices.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 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".