Drawing on antiracist approaches toward a critical antidiscriminatory pedagogy for nursing
Bibliographic record
Abstract
Although nursing has a unique contribution to advancing social justice in health care practices and education, and although social justice has been claimed as a core value of nursing, there is little guidance regarding how to enact social justice in nursing practice and education. In this paper, we propose a critical antidiscriminatory pedagogy (CADP) for nursing as a promising path in this direction. We argue that because discrimination is inherent to the production and maintenance of inequities and injustices, adopting a CADP offers opportunities for students and practicing nurses to develop their capacity to counteract racism and other forms of individual and systemic discrimination in health care, and thus promote social justice. The CADP we propose has the following features: it is grounded in a critical intersectional perspective of discrimination, it aims at fostering transformative learning, and it involves a praxis-oriented critical consciousness. A CADP challenges the liberal individualist paradigm that dominates much of western-based health care, and the culturalist and racializing processes prevalent in nursing education. It also situates nursing practice as responsive to health inequities. Thus, a CADP is a promising way to translate social justice into nursing practice and education through transformative learning.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| 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".