Intégration des droits humains dans la pratique du personnel infirmier faisant usage de coercition en santé mentale : recension systématique des écrits et méta-ethnographie
Bibliographic record
Abstract
Introduction and background : The last decade has seen a steady and rising use of coercion in mental health care, as well as an increase in the number of forms it takes. The application of these measures frequently relies on the work of nurses, but few studies have analyzed the human rights issues raised by these practices.Aim : To produce a qualitative synthesis of how human rights are integrated into the practice of nurses who use coercion in mental health care.Methodology : A systematic review of qualitative scientific literature published between 2008 and 2018 was conducted and supplemented by a meta-ethnographic analysis.Results : The analysis of the forty-six selected studies revealed four distinct themes : coercion in mental health care as a socio-legal object, issues of recognition of human rights in mental health care, role conflict experienced by nurses, and the conceptualization of coercion as a necessary evil or a critical incident.Discussion and conclusion : Further research is needed to understand the specifics of the continuum of support and control that characterizes the coercive work of psychiatric nurses.
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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 | 0.051 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".