The Challenge of Diversity in Nursing Leadership: The Need to Avoid Misinformation and False Facts
Bibliographic record
Abstract
This article probes two recent (2022-2023) instances of false narratives in nursing, both with the laudable goal of promoting diversity in nursing leadership, but which presentations are flawed by false facts. The first example is the installation of portraits of Florence Nightingale, the founder of nursing, and Mary Seacole, the Crimean War businesswoman and volunteer, at Toronto’s University Health Network and Princess Margaret Cancer Centre. The second is a 2023 article by a nursing academic, Jennifer Woo, urging decolonization of “the history of nursing by magnifying the contributions of nurses of colour”. It includes mention of a number of leading nurses of colour, however with an enormous number of false claims for Mary Seacole. Brief mention is also made of Rappaport’s 2022 revised book on Seacole calling her “a Black Cultural Icon and Humanitarian”. This article goes on to present two leading diversity nurses well worthy of celebration: The Nigerian Kofoworola Abeni Pratt, the first Black nurse in Britain’s National Health Service, who led in Nigerian nurses assuming leadership roles from white, British, expatriate nurses. The other is the redoubtable Mary Elizabeth Carnegie (1916-2008), the African-American nurse who led in the racial integration of nursing in the United States. Consideration is given as well to how such major mistakes are made in the nursing literature, with a recommendation for much more critical reading of sources.
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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 | 0.100 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.030 | 0.151 |
| Scholarly communication | 0.038 | 0.088 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.021 | 0.046 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".