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Record W4395112709 · doi:10.3138/jcs-2022-0038

The Work-Life Experiences of Black African Immigrant Nurses in Vancouver: Everyday Racisms and Acts of Resistance

2023· article· en· W4395112709 on OpenAlexaffvenueabout
Maureen Kihika

Bibliographic record

VenueJournal of Canadian Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsImmigrationResistance (ecology)SociologyEveryday lifeGender studiesWork (physics)EthnologyAnthropologyGeographyPolitical scienceArchaeologyLaw

Abstract

fetched live from OpenAlex

This analysis is based on semi-structured interviews examining the work–life experiences of Black African immigrant nurses in Vancouver, British Columbia, conducted from June 2013 to June 2014. The article argues that nurses experience systemic barriers in which their sense of Canadian belonging and professionalism are called into question by patients, colleagues, and managers. Using the framing device of everyday racism, findings suggest that nurses navigate and counter these socio-cultural barriers by repurposing their daily actions into powerful subversive acts of resistance. The article uses the concept of everyday racism to relate the day-to-day experiences recounted by Black nurses to the larger macrostructural contexts that define the intersecting inequalities they describe. Grounded in Black Canadian feminist theory, this article contends that the lives of Black nurses offer critical insights to challenge structures of dominance. This article builds on existing scholarship discussing experiences of racism among Black and largely Caribbean nurses in Canada. The contribution is important because it offers the opportunity to analyze the lived realities of continental African nurses in Vancouver, in the historical context of a racialized Canadian state policy.

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 score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.331
Teacher spread0.294 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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