Black nurses in action: A social movement to end racism and discrimination
Bibliographic record
Abstract
We bear witness to a sweeping social movement for change-fostered and driven by a powerful group of Black nurses and nursing students determined to call out and dismantle anti-Black racism and discrimination within the profession of nursing. The Black Nurses Task Force, launched by the Registered Nurses' Association of Ontario (RNAO) in July 2020, is building momentum for long-standing change in the profession by critically examining the racist and discriminatory history of nursing, listening to and learning from the lived experiences of the Black nursing community, and shaping concrete, actionable steps to confront anti-Black racism and discrimination in academic settings, workplaces, and nursing organizations. The Black Nurses Task Force and the RNAO are standing up and speaking out in acknowledgment of the magnitude of anti-Black racism and discrimination that exist in our profession, health system, justice system, and economic system. This social movement is demonstrating, in actions, how individuals and a collective act as change agents to drive meaningful and widespread change for our present and future Black nurses. We also acknowledge the Black nurses who have gone before us.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.055 | 0.055 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 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".