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Record W2032989635 · doi:10.1111/nin.12028

Conceptualizing structural violence in the context of mental health nursing

2013· article· en· W2032989635 on OpenAlexaffabout
Jacqueline Choiniere, Judith A. MacDonnell, Andrea Louise Campbell, Sandra Smele

Bibliographic record

VenueNursing Inquiry · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsYork University
Fundersnot available
KeywordsStructural violenceRedressBlameReductionismNormalization (sociology)Context (archaeology)SociologyMental healthHealth carePower (physics)Face (sociological concept)PsychologySocial psychologyNursingMedicinePolitical scienceSocial sciencePoliticsEpistemologyPsychiatry

Abstract

fetched live from OpenAlex

This article explores how the intersections of gendered, racialized and neoliberal dynamics reproduce social inequality and shape the violence that nurses face. Grounded in the interviews and focus groups conducted with a purposeful sample of 17 registered nurses (RNs) and registered practical nurses (RPNs) currently working in Ontario's mental health sector, our analysis underscores the need to move beyond reductionist notions of violence as simply individual physical or psychological events. While acknowledging that violence is a very real and disturbing experience for individual nurses, our article casts light on the importance of a broader, power structure analysis of violence experienced by nurses in this sector, arguing that effective redress lies beyond blame shifting between clients/patients and nurses. Our analysis illustrates how assumptions about gender, race and care operate in the context of global, neoliberal forces to reinforce, intensify and create, as well as obscure, structural violence through mechanisms of individualization and normalization.

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 score0.250
Threshold uncertainty score0.586

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 studies0.0010.001
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 head0.082
GPT teacher head0.438
Teacher spread0.356 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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