MétaCan
Menu
Back to cohort
Record W4400877214 · doi:10.1111/nin.12661

The Subaltern: Illuminating matters of representation and agency in mental health nursing through a postcolonial feminist lens

2024· article· en· W4400877214 on OpenAlexaff
Shivinder Dhari, Allie Slemon, Emily Jenkins

Bibliographic record

VenueNursing Inquiry · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaCamosun College
Fundersnot available
KeywordsSubalternAgency (philosophy)Mental healthGender studiesPatriarchySociologyPower (physics)HarmNarrativeColonialismFeminismPsychologySocial psychologyPoliticsPolitical scienceSocial sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

Inpatient mental health nursing operates with an overarching goal to support people living with mental health challenges by managing risk of harm to self and others, decreasing symptoms, and promoting capacity to live outside of hospital settings. Yet, dominant, harmful stereotypes persist, constructing patients as less than, in need of saving, and lacking self-control and agency. These dominant assumptions are deeply entrenched in racist, patriarchal, and Othering beliefs and continue to perpetuate and (re)produce inequities, specifically for people with multiple intersecting identities relating to race, class, gender, and culture. This paper explores the relevance of postcolonial feminism, particularly Gayatri Spivak's concept of Subaltern-conceptualized as groups of people who are denied access to power and therefore continue to be systematically oppressed and marginalized-in illuminating the problematic and dominant assumptions about people living with mental health challenges as lacking agency and requiring representation. Through an understanding of Subalternity, this paper aims to decenter and deconstruct dominant colonial, patriarchal narratives in mental health nursing, and ultimately calls for mental health nursing to fundamentally reconsider prevailing assumptions of patients as needing representation and lacking agency.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.427

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 head0.063
GPT teacher head0.428
Teacher spread0.365 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNursing InquirySame topicMigration, Health and TraumaFrench-language works237,207