Formal Coercion and the Moral Division of Labor: Moving Beyond Role Conflicts in Psychiatric Nursing
Bibliographic record
Abstract
INTRODUCTION: Psychiatric nurses are actively involved in involuntary hospitalisations and treatments. However, the scientific literature lacks insights into strategies used to navigate roles and institutional expectations in this regard. AIM: To achieve a deeper understanding of practices that support the exercise of individual rights in psychiatry during involuntary hospitalisations and treatments. METHOD: A situational analysis was conducted. Nineteen (n = 19) mental health workers participated in a semi-structured interview and completed a socio-demographic questionnaire. Case law review (n = 126) and non-participant observations (n = 70 h) were also conducted. RESULTS: The moral division of labour is a central element in mental health interventions, organising four types of practices: socio-legal assistance, support for exercising rights, intersectoral coordination and ignorance and trivialisation. DISCUSSION: Our findings reveal that nursing care is predominantly medicalised, confining nurses to a subordinate role. They also indicate marginal practices focused on socio-legal assistance and human rights support. Intersectoral coordination, though crucial, remains an invisible aspect of nursing practice. IMPLICATIONS FOR PRACTICE: There remains a significant lack of awareness about human rights issues in psychiatry. This research underscores the importance of considering professional hierarchies, organisational expectations and legal awareness to address role conflicts in psychiatric care. RELEVANCE STATEMENT: This research highlights the need to study nurses' agency, offering insights into how they interpret and apply psychiatric laws. Such research could deepen knowledge on human rights issues in psychiatry or, at minimum, promote greater recognition of these concerns. Social policy implications are also notable, underscoring the need for integrated socio-legal assistance during involuntary treatments to support patients navigating complex systems and exercising their rights. Finally, the findings point to the importance of examining professional hierarchies, organisational expectations and legal awareness in developing practices that address the emotional, educational and intersectoral aspects of psychiatric coercion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".