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Record W4206094924 · doi:10.1177/00084174211066676

Systemic Racism in Canadian Occupational Therapy: A Qualitative Study with Therapists

2022· article· en· W4206094924 on OpenAlexfundvenueaboutno aff
Brenda L. Beagan, Kaitlin R. Sibbald, Stephanie R. Bizzeth, Tara Pride

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsRacismCoping (psychology)Institutional racismIndigenousPsychometrics of racismResistance (ecology)PsychologyOccupational therapySociologyGender studiesPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Background. Research on racism within occupational therapy is scant, though there are hints that racialized therapists struggle. Purpose. This paper examines experiences of racism in occupational therapy, including coping strategies and resistance. Method. Ten therapists from racialized groups (not including Indigenous peoples) were recruited for cross-Canada, in-person or telephone interviews. Transcripts were coded and inductively analysed, with data thematically organized by types of racism and responses. Findings. Interpersonal racism involving clients, students, colleagues and managers is supported by institutional racism when incidents of racism are met with inaction, and racialized therapists are rarely in leadership roles. Structural racism means the experiences of racialized people are negated within the profession. Cognitive sense-making becomes a key coping strategy, especially when resistance is costly. Implications. Peer supports and community building among racialized therapists may be beneficial, but dismantling structures of racism demands interrogating how whiteness is built into business-as-usual in occupational therapy.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.332
GPT teacher head0.545
Teacher spread0.214 · 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 designObservational
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

Citations38
Published2022
Admission routes3
Has abstractyes

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