Impact of Trump's Executive Order on Nursing Research: The Shrouding of Racism Under the Guise of Equality
Bibliographic record
Abstract
On January 20, 2025, Trump signed an executive order specifying that each American deserves "equal dignity and respect" and that all "discriminatory" programs be terminated including mandates, policies, action plans, and grants related to "diversity, equity, inclusion" (EDI) (The White House, 2025).We are concerned that this order reflects white supremacy and how systemic racism can manifest under the guise of promoting equality, effectively undermining efforts to address inequities that impact racialized and marginalized communities.So, why are we talking about this in a nursing research journal in Canada?To begin, we believe this executive order has plunged us backwards into the flawed thinking that equates equity with equality, a debate we thought was long over.The World Economic Forum (2023) distinguishes equality as providing the same resources and opportunities to all while equity refers to tailoring these to the needs and circumstances of each person.In healthcare, we discuss equity in terms of reducing unfair and unjust differences so that fair opportunities are provided and each person may "reach their fullest health potential" (Public Health Ontario, 2023, p. 2).While we agree that everyone deserves equal dignity and respect, we strongly reject the notion that terminating EDI mandates and initiatives achieves this goal.Far from being discriminatory, EDI is essential for addressing historical injustices, systemic inequities, and fostering true fairness.To return to why this executive order is relevant to Canada: first and most simply, what happens in the United States has the potential to influence our culture, leaders, and policies.Although our healthcare systems are strikingly different in terms of funding and access, Canada and the United States have generally demonstrated values related to social justice.However, this order raises pressing questions for Canadians: How might it shape our daily lives and institutions, particularly in healthcare, education, and research?Specific to nursing research, the principles of EDI are salient to promoting health equity.As a profession, nursing plays a central role in dismantling racism (Boakye et al., 2024;Montague et al., 2024) and has worked to do so through research.The effects of racism are detrimental to
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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 | 0.047 | 0.149 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.027 | 0.012 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.059 | 0.063 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".