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Record W4307210294 · doi:10.1503/cmaj.221503

Black Health Education Collaborative: the important role of Critical Race Theory in disrupting anti-Black racism in medical practice and education

2022· article· en· W4307210294 on OpenAlexaffvenueabout
Delia D. Douglas, Sume Ndumbe-Eyoh, Kannin Osei-Tutu, Barbara Hamilton-Hinch, Gaynor Watson-Creed, Onye Nnorom, OmiSoore H. Dryden

Bibliographic record

VenueCanadian Medical Association Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of ManitobaPublic Health OntarioDalhousie UniversityUniversity of TorontoToronto Public HealthUniversity of Calgary
Fundersnot available
KeywordsRacismRace (biology)Critical race theoryInstitutional racismMedicineSociologyGender studies

Abstract

fetched live from OpenAlex

KEY POINTS Anti-Black racism is real, widespread and affects the lives of Black people in Canada. It is a daily reality that co-occurs and affects all other health conditions that Black people experience. [1][1],[2][2] It is imperative that the realities of anti-Black racism be deliberately engaged

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)0.053
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0320.032
Scholarly communication0.0200.015
Open science0.0030.021
Research integrity0.0090.023
Insufficient payload (model declined to judge)0.0170.001

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 head0.010
GPT teacher head0.420
Teacher spread0.410 · 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 designTheoretical or conceptual
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

Citations18
Published2022
Admission routes3
Has abstractyes

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