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Record W4313309323 · doi:10.25071/2291-5796.125

Racialized-gendered Experiences and Mental Health Vulnerabilities of Young Asian Women in Toronto, Canada

2022· article· en· W4313309323 on OpenAlexaffvenueabout
Krisel Abulencia, Coralee McLaren, Mandana Vahabi, Josephine Pui‐Hing Wong

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGender studiesMental healthOppressionRacismSociologyDiasporaNarrativePsychologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Discourses of mental health vulnerabilities of women in the Asian diaspora s are often invoked through the concepts of “culture” with little consideration of asymmetric power relations and structural influences. We used a narrative approach to explore the experiences and perspectives on culture, identities, relationships, and mental health among young Asian women living in Toronto, Canada. We engaged 14 participants in focus groups and individual interviews, and identified four overall themes: (1) racialized-gendered bodily abjection, (2) experiences of enacted racism and sexism, (3) perceptions of familial expectations, and (4) their strategies of coping and resilience. Our analysis revealed how Whiteness and structural violence shape the racialized-gendered experiences of young Asian women and perpetuate microaggressions that compromise their mental health and well-being. Critical nursing practice must question the idea of “culture” embedded in the dominant discourse of “culturally competent” care. Nurses need to achieve structural competence to dismantle systems of oppression and unequal power relations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.352
Teacher spread0.336 · 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 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

Citations0
Published2022
Admission routes3
Has abstractyes

Explore more

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