Moving beyond the rhetoric of social justice in nursing education
Bibliographic record
Abstract
We argue that while the discipline of nursing aligns with the ideals of social justice and anti-racism, it has been challenging for nurse educators to translate these ideals into practice. In this discussion paper, we explore these challenges. Of note, there is little guidance for nurse educators to support student knowledge development in addressing the complex issues surrounding anti-racist and anti-discriminatory practice. Accordingly, we utilized Peggy Chinn’s Peace and Power framework as a guide to develop an anti-racist practice that is underpinned by critical pedagogy. Our aim is to provide teaching and learning strategies for nurse educators to address racism, discrimination, and oppression in undergraduate nursing learning environments. Implications of this article include guidance for nurse educators who are committed to anti-racist pedagogical practice.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.015 |
| 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".