Still a Long Way to Go: Integrating Antiracist, Anti-oppressive Education in Nursing
Bibliographic record
Abstract
Systemic racism is evident in the racialized health outcomes of Indigenous patients in Canada and the Truth and Reconciliation Commission’s (2015) call to action number 24 urges nursing schools to provide antiracism training. Are nursing programs and faculty prepared for inclusion of antiracism education? White nurses are complicit participants in systemic racism, yet our current educational focus on culture cannot adequately address the ongoing racism. Since racism is a systemic problem, solutions must involve policy change. Despite availability of a body of antiracist, anti-oppressive literature developed and used in professions such as teaching, nurses are not currently being equipped to practice identifying and naming oppression so that our own complicity can be dismantled and so that we can teach antiracism to nursing students. Therefore, this research project sought to support nursing faculty by partnering with a community antiracism organization to pilot a workshop introducing antiracist, anti-oppressive education. A small group of white nursing faculty participated in a focus group interview reflecting on the workshop. The transcript data was analyzed using the methodology of poststructural discourse analysis grounded in critical race theory and critical whiteness studies. The analysis seeks to answer the research question: How do white nursing faculty construct themselves, Others, and antiracist education? The findings demonstrated that the participants constructed racial Others and themselves in particular ways consistent with the broader patterns of whiteness in antiracism literature. Participants also demonstrated particular understandings of antiracism education and pointed toward further support they need. The implications of these findings are considered at the level of white faculty members so as to prepare for broader antiracism policies and initiatives within nursing programs.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".