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Record W3176269731 · doi:10.25071/2291-5796.73

Hiding in Plain Sight

2021· article· en· W3176269731 on OpenAlexvenueno aff
Vijeta Venkataraman, Trudy Rudge, Jane Currie

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceNursingIntervention (counseling)Focus groupWork (physics)Perspective (graphical)PsychologyMedicinePublic relationsSociologySuicide preventionPoison controlPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

The incidence of Intimate Partner Violence (IPV) in Australia is rising. Women experiencing IPV seek assistance through Emergency Departments (ED). Women exhibit help-seeking behaviours to nurses who work in emergency over medical or allied health professionals. Nurses’ capacity to recognise the need to care for women experiencing IPV is essential. The aim of this study was to explore nurses’ capacity to care for women who have experienced IPV through outlining inhibiting factors that limit care and create a discourse that contributes to addressing these factors. Pre (n=10) and post (n=6) focus groups (FGs) were undertaken with nurses who work in ED. In between the FGs an intervention was applied to prompt change to caring practices. The discourse generated from the FGs was subjected to a Foucauldian discourse analysis from a poststructural feminist perspective. Participants’ capacity to care was found to be based on the values they formed on IPV, as shaped by their post-registration training. The formation of boundaries was fundamental in inhibiting the participants’ capacity to care. Challenging boundaries through educational inquiry into nursing values can be effective in shifting perspectives of IPV. The raising of awareness of IPV in our communities serves as a vital tool in eliciting cultural behaviour change within EDs and within nursing culture.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
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 head0.033
GPT teacher head0.380
Teacher spread0.348 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueWitness The Canadian Journal of Critical Nursing DiscourseSame topicIntimate Partner and Family ViolenceFrench-language works237,207