Navigating the Unseen Strain: The Hidden Challenges of Black Nursing Faculty in the Fight Against Anti-Black Racism
Bibliographic record
Abstract
As Black faculty members of a majority-White nursing school, we reflected on our unique experiences as part of a Black community engagement project, aimed at addressing anti-Black racism in nursing education. Our positionality created a complex scenario as we navigated emotionally heavy discussions, grappled with our ability to manage competing interests and care for our own well-being. The invisibility of the undue burden of anti-racism work is discussed. Recommendations for alleviating the burden are proposed based on this lived experience. Nursing schools must recognize the uniqueness of Black faculty members' experiences. Proposed strategies include creating mechanisms and resources for emotional support and incorporating anti-racism initiatives into the nursing school's values and strategic plan.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".