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Record W1845195412

[Identification and management of violence in psychiatry: Nurse and patient perceptions of safety and dangerousness].

2015· article· en· W1845195412 on OpenAlexaboutno aff
Amélie Perron, Jean Daniel Jacob, Louise Beauvais, Danielle Corbeil, David M. Berube

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
Fundersnot available
KeywordsApprehensionAggressionFeelingNursingPsychiatryPerceptionPsychologySuicide preventionVulnerability (computing)Poison controlMedicineClinical psychologyMedical emergencySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

This paper reports the results of a study on the identification and management of violence on a psychiatric ward and in the psychiatric emergency of a Quebec hospital. The purpose of this exploratory and descriptive study was to examine patients' and nurses' perceptions and strategies for identifying and managing patient aggression and violence. Results show that the type of setting influences the way aggressive behaviour issues are perceived and managed. The types of behaviours deemed aggressive or risky also vary between the two units. Moreover, patients and nurses are similarly described by all participants as susceptible to being violent and to being a victim of violence. Prevention of aggression and violence remains a significant challenge in psychiatric nursing, where administrative and environmental constraints, the growing complexity of clinical profiles, divergent interprofessional approaches to care, and collective feelings of apprehension and vulnerability interact.

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)0.004
metaresearch head score (Gemma)0.020
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 score0.163
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.035
GPT teacher head0.348
Teacher spread0.313 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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