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Record W2164164421 · doi:10.3917/rsi.120.0047

Identification et gestion de la violence en psychiatrie : perceptions du personnel infirmier et des patients en matière de sécurité et dangerosité

2015· article· fr· W2164164421 on OpenAlexaffabout
Amélie Perron, Jean Daniel Jacob, Louise Beauvais, Danielle Corbeil, David M. Berube

Bibliographic record

VenueRecherche en soins infirmiers · 2015
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsCentre de Santé et de Services Sociaux de ChicoutimiSt Mary's Hospital CentreUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Cet article présente les résultats obtenus dans le cadre d’une recherche portant sur l’identification et la gestion de la violence sur une unité psychiatrique et à l’urgence psychiatrique d’un hôpital québécois. Cette étude exploratoire et descriptive visait à examiner les perceptions et les stratégies de prévention et de gestion du personnel infirmier et des patients vis-à-vis de l’agressivité et de la violence manifestées par des patients. Les résultats indiquent que le type de milieu influence la manière dont sont perçues et prises en charge les problématiques liées aux comportements agressifs. Les types de comportements jugés agressifs ou à risque diffèrent également d’une unité à l’autre. Par ailleurs, tant les patients que le personnel soignant sont décrits par tous les participants comme étant susceptibles de se comporter de manière violente et de subir les contrecoups de la violence. La prévention de l’agression et de la violence demeure un défi de taille en soins infirmiers psychiatriques, où se mêlent contraintes administratives et environnementales, complexification des cas cliniques, divergences interprofessionnelles et sentiments collectifs d’appréhension et de vulnérabilité.

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)0.040
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0000.001

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 head0.174
GPT teacher head0.476
Teacher spread0.302 · 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 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

Citations11
Published2015
Admission routes2
Has abstractyes

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