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Record W4315435500

Intervening against the patient’s wishes: pro re nata medication and the phenomenological experience of nurses working in a forensic psychiatry environment

2022· article· en· W4315435500 on OpenAlexaffabout
Charlène Seyer-Forget, Dave Holmes, Jean Daniel Jacob, Emmanuelle Bernheim, Étienne Paradis-Gagné

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité de MontréalUniversity of OttawaInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsForensic psychiatryCommitPsychologyPsychoanalysisNursingMedicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

In forensic psychiatry environments, nurses are regularly confronted with the use of restrictive measures such as chemical restraints and are forced to constantly navigate between care and social control. The debate over the ethics of coercion and the use of control measures for violence in psychiatric settings is far from resolved. The objective of this study is to understand the ethical experience of nurses in a Canadian forensic psychiatry environment when administering PRN (when required) medication against the patient's will. The experiences of 14 nurses are analyzed from a critical ethical perspective through interpretive phenomenological analysis. Across the three main categories-certainties, paradoxes, and learning-the results show that nurses must simultaneously commit their allegiance to the patient, to the justice system, and to the culture of the "total" institution. These multiple allegiances generate paradoxes that affect the way nurses actualize their professional role.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.039
Scholarly communication0.0080.006
Open science0.0020.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.056
GPT teacher head0.304
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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