Commitment to Positive Change: Structural Anti-racism Audit of Nursing Education Programs
Bibliographic record
Abstract
Amidst many opportunities to create positive change and examine systemic anti-racist decolonial practices (Moorley et al., 2020), we are advocating for concrete action at the root of Nursing education programs by way of a structural anti-racism audit. Based on decolonial and antiracist theory (Garneau et al, 2018; Gaudry & Lorenz, 2018; Kendi, 2019; McGibbon & Etowa, 2009), we propose to engage in systems-level action (McGowan et al, 2020; Mulgan, 2006; van Wijk t al., 2018) and examine institutional structures through an anti-racist framework (Sutton, 2002) based on audit processes for equity, diversity, and inclusion (Chun & Evans, 2019; Olson, 2020; Skrla et al., 2004; Skrla et al., 2009; Zion, et al., 2020). Structures within and influencing curriculum, pedagogy, evaluation will be examined to advance systems-level anti-racist practices and policies (Moorley et al., 2020) with Nursing students, faculty, staff, leadership as a foundation for equitable Nursing education and care (National Collaborating Centre for the Determinants of Health, 2014). This anti-racist approach to Nursing education reform promises to address the pernicious harms of discrimination in the healthcare system, as noted in a recent report on Indigenous-specific racism (Turpel-Lafonde, 2020). We aim to conduct a strengths-based structural anti-racism audit that does not lose sight of disparities (Fogarty et al., 2018). We are currently conducting a literature review and audit framework development and will pilot the structural anti-racism audit in fall 2021. Rather than requesting endorsement of our project, and with respect for diverse approaches, we asked Nursing colleagues to sign this letter to demonstrate shared commitment to critically examine racist challenges and anti-racist opportunities in their Nursing program at a structural level (see this survey: https://forms.gle/tZPN2z1kUoARNPp1A
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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 | 0.105 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".