The mobilization of anti-racism here is like squeezing a square peg through a round hole: A critical qualitative inquiry into anti-racism in settler-colonial Canadian nursing education
Bibliographic record
Abstract
Anti-racism is a relatively new priority for nursing education. The political mandate to act on the calls to action from the Truth and Reconciliation Commission of Canada Report and the legislation of the Declaration of the Rights of Indigenous Peoples Act in British Columbia, have made this so. This dissertation represents a qualitative study of anti-racism in Canadian nursing education from the standpoint of baccalaureate nurse educators. The purpose of the study was to map the landscape of anti-racism in schools of nursing to identify strengths, gaps, and failings so individuals and schools can accountably develop action plans. The research questions were: How do Canadian nurse educators engage in anti-racism in their work? What are the structural and discursive barriers to urgently addressing racial discrimination or hegemonic race ideology? And the sub-question: How do participants understand the influence their identities have on their anti-racism knowledge and practice? Data were collected via an online questionnaire and online focus groups in 2021, and was analyzed through a contextualist content analysis. Educators engage in anti-racism through the content they teach, and less so through their instructional methods but are held back by myriad structural and pedagogical barriers. Anti-racist pedagogy remains poorly articulated in nursing curricula and by nurse educators, is often relegated to discrete, one-time courses, and can be confused with Indigenization. Job precarity and institutional resistance act as structural barriers to anti-racism. Nurse educators navigate and participate in cultural and institutional Whiteness which limits the reach and authenticity of any anti-racist effort. Twenty-six structural and pedagogical recommendations are offered. This dissertation is presented in journal article format.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.053 | 0.061 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".