Is teaching anti-Black racism relevant when recreating a post-COVID nursing curriculum?
Bibliographic record
Abstract
During the COVID-19 pandemic several issues were galvanized as global urgencies. One of which was racism, following reports that Black and low-income communities were disproportionately impacted by the pandemic (Public Health Agency of Canada, 2021) and the lack of race-based data in Canada (Ahmed et al., 2021). But it was the racially induced killing of George Floyd and others that brought global awareness through the Black Lives Matter movement of the extent of structural and institutional racism. We witnessed a convergence of protests regarding anti-Black racism, anti-Indigenous racism, anti-Asian racism, and more recently Islamophobia. These series of events have led to emerging and compelling questions from millennials and Generation Zs within the nursing classroom. Nursing education is called to embrace and draw upon multiple forms of pedagogies, methodologies, and theories that reflect and support student learning and enquiry (Coleman, 2020; Prendergast et al., 2020;).
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".