‘Ain't I a Nurse’, implementing a digital illustration of resistance when challenging anti‐Black racism in nursing education
Bibliographic record
Abstract
Since the COVID-19 pandemic, ongoing reports have highlighted the urgency of addressing anti-Black racism within Canada's healthcare system. The paucity of research within a Canadian context has created growing concerns among Millennials and Generation Zs for healthcare to address growing health disparities and health inequities that are attributed to institutional and structural racism. Recognizing the paradigm shift that has occurred because of the pandemic and the sleuth of racial killings, the nursing classroom has witnessed a change and a need for nursing education to be relevant for the cohort of nursing students who are seeking answers. The scarcity of nursing literature addressing diverse forms of learning demonstrates the need for nursing education to explore new ways of being diverse, inclusive and innovative when teaching intergenerationally. In this paper, the author challenges nurse educators to revisit the student-educator relationship by introducing critical digital pedagogy to dismantle anti-Black racism and promote student-educator engagement for transformative learning to occur. As an educator, the author implements the use of digital illustration as a tool of resistance for students and educators to assess, engage, act and reflect on creating change within nursing education. Using Black feminist thought and culturally responsive learning, the author introduces an arts-based approach through the innovative design of an illustration, titled, 'Ain't I a Nurse. Combining historical stories with contemporary socio-political experiences, the author demonstrates how students and educators can enter a cognitive learning experience where they can connect mentally and emotionally, and in so doing re-envision and recreate a new world that centralizes equity, diversity and inclusivity through critical discourses. Through the illustration anti-Black racism is challenged and anti-Black racism resistance is discovered as an antidote in dismantling anti-Black racism within nursing education.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".