Bibliographic record
Abstract
This article relates the flagrant instances of misinformation on Florence Nightingale, the major founder of professional nursing, in 2020, the bicentenary of her birth, and 2021. It notes the new trend to “pair” Nightingale with another supposed “nursing pioneer,” who was a businesswoman and generous volunteer, Mary Seacole, but who never portrayed herself as a nurse. The article goes on to cite the promotion of misinformation on the two by no less than the Queen, in her Christmas message of 2020, and by her heir, the Prince of Wales, on 12 May 2021, Nightingale’s birthday and International Nurses Day. The most extreme example of misinformation is that of the prince, who claimed joint status for Seacole with Nightingale in achieving the sanitary reforms in the Crimean War that saved large numbers of lives. Unlike Seacole, Nightingale played a role in these reforms, but credited the doctors and engineers of the Sanitary Commission who did the heavy work of renovation. The article calls for high standards of ethics and scholarship in nursing and health care publication. Health authorities, such as Britain’s National Health Service, should be the source of reliable information, especially in a pandemic. Misinformation on mere “historical” matters, not clinical, is not acceptable. Diversity and inclusion are valid goals of any health care system, but should be pursued with integrity. The article introduces a fine Black nursing leader, Kofoworola Abeni Pratt, who is ignored and yet should be celebrated for her contributions to nursing both in England and her home country, Nigeria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.163 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.027 | 0.148 |
| Scholarly communication | 0.050 | 0.048 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".