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Record W2548735606 · doi:10.1080/09638237.2016.1207222

Aggression in psychiatric hospitalizations: a qualitative study of patient and provider perspectives

2016· article· en· W2548735606 on OpenAlexaff
Denise Lamanna, Danijela Ninkovic, Vinothini Vijayaratnam, Ken Balderson, Harold Spivak, S. Brook, David W. Robertson

Bibliographic record

VenueJournal of Mental Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsAggressionPsychiatryQualitative researchPsychologyMedicinePoison controlClinical psychologyMedical emergencySociology

Abstract

fetched live from OpenAlex

BACKGROUND: When the people hospitalized in psychiatric units demonstrate aggression, it harms individuals and creates legal and financial issues for hospitals. Aggression has been linked to inpatient, clinician and environmental characteristics. However, previous work primarily accessed clinicians' perspectives or administrative data and rarely incorporated inpatients' insights. This limits validity of findings and impedes comparisons of inpatient and clinician perspectives. AIMS: This study explored and compared inpatient and clinician perspectives on the factors affecting verbal and physical aggression by psychiatric inpatients. METHODS: This study used an interpretive theoretical framework. Fourteen inpatients and 10 clinicians were purposefully sampled and completed semi-structured interviews. Data were analyzed using inductive thematic analysis. RESULTS: Six themes were identified at personal and organizational levels. The three person-level themes were major life stressors, experience of illness and interpersonal connections with clinicians. The three organization-level themes were physical confinement, behavioural restrictions and disengagement from treatment decisions. CONCLUSIONS: Aggression is perceived to have a wide range of origins spanning personal experiences and organizational policies, suggesting that a wide range of prevention strategies are needed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.132

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.402
Teacher spread0.384 · 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 teacher head, 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

Citations33
Published2016
Admission routes1
Has abstractyes

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