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Record W1984673995 · doi:10.1177/000841740907600407

The Impact of Everyday Racism on the Occupations of African Canadian Women

2009· article· en· W1984673995 on OpenAlexafffundvenueabout
Brenda L. Beagan, Josephine Etowa

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsDalhousie University
FundersNova Scotia Health Research Foundation
KeywordsRacismGender studiesSociologyOccupational sciencePsychologyOccupational therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational therapy has increasingly explored the impact of cultural differences on occupations but has not yet begun to explore the impact of racism on human occupation. PURPOSE: This study with 50 African Canadian women used mixed methods to explore the effects of racism on their occupational experiences. METHODS: Women aged 40-65 were interviewed in-depth about everyday experiences with racism and overall well-being. Three standardized instruments assessed frequency and stressfulness of race-related experiences. FINDINGS: Everyday racism had subtle, almost intangible, impacts, shaping women's engagement with and the meaning of leisure, productive, and caring occupations. IMPLICATIONS: As occupational therapy increasingly attends to issues of cultural difference, it is critical to also attend to racism. This means learning to ask thoughtful questions about how racism may shape clients' occupations. Attention to this aspect of the social environment will enhance practice with African-heritage clients and clients from other racial minority groups.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.114
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.005
Scholarly communication0.0020.000
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.249
GPT teacher head0.509
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2009
Admission routes4
Has abstractyes

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