Building Solidarity with Black Nurses to Dismantle Systemic and Structural Racism in Nursing
Bibliographic record
Abstract
Systemic and structural racism in nursing have profound impacts on Black People, Indigenous Peoples, and People of Color. They contributed to underrepresentation in faculty, senior nurse executives, and presidents' positions in academic and healthcare organizations, physical and mental health issues in racialized groups. This quality improvement study described ways in which the Black Nurses Task Force of the Registered Nurses Association of Ontario can build solidarity with nursing and government organizations to dismantle systemic and structural racism in nursing. This study used a structured online survey, comprised of quantitative and qualitative questions. The qualitative data were analyzed using interpretative thematic analysis and the quantitative data were analyzed with descriptive statistics. Findings showed that 88% of participants experienced racism and 63% said racism affected their mental health. Three themes emerged from the qualitative data: Social support for Black nurses, accountability of leaders and solidarity with Black nurses. These findings demonstrated the urgent need to dismantle systemic and structural racism in nursing.
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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 | 0.032 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".