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Record W2943583469 · doi:10.3917/rsi.128.0041

Contentions mécaniques en psychiatrie : étude phénoménologique de l’expérience vécue du personnel infirmier

2017· article· fr· W2943583469 on OpenAlexaffabout
Pascale Corneau, Jean Daniel Jacob, Dave Holmes, Désiré Rioux

Bibliographic record

VenueRecherche en soins infirmiers · 2017
Typearticle
Languagefr
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsCanadian Nurses AssociationUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

L’usage des contentions mécaniques dans les milieux psychiatriques fait aujourd’hui l’objet de nombreuses controverses éthiques. Toutefois, on note l’absence des voix des patients et du personnel infirmier en regard de cette intervention controversée. L’objectif de cette étude qualitative était d’examiner l’expérience vécue du personnel infirmier exerçant en psychiatrie faisant usage de la contention mécanique. Vingt-et-un(e) infirmier(e)s travaillant sur les unités de psychiatrie et d’urgence psychiatrique d’un centre hospitalier universitaire canadien ont participé à des entrevues semi-dirigées, qui ont ensuite été transcrites, codées et analysées selon la méthode d’analyse interprétative phénoménologique (AIP). Trois thèmes principaux ont été identifiés : 1) contexte de pratique ; 2) processus de contentions ; et 3) recourir à la contention mécanique. Les résultats de cette recherche phénoménologique mettent en lumière les défis organisationnels et émotionnels auxquels est confronté le personnel infirmier exerçant en psychiatrie. Les perspectives de Foucault et Goffman ont été les sources théoriques primaires qui ont guidé le processus d’analyse critique mené lors de cette recherche qualitative.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0010.001

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 head0.258
GPT teacher head0.508
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2017
Admission routes2
Has abstractyes

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