Florence Nightingale and Mary Seacole: Which is the forgotten hero of health care and why?
Bibliographic record
Abstract
BACKGROUND AND AIMS: This paper aims at correcting misinformation in circulation portraying Mary Seacole as a nurse, Crimean War heroine, and health care pioneer, even, for some, a replacement for Florence Nightingale, who really was a health care pioneer as well as being the major founder of the modern profession of nursing. METHODS AND RESULTS: The article focuses on the claims for Seacole made by C. Short in Scottish Medical Journal, 2011. It reports, using primary sources, on what Seacole actually did--running a business for officers, with kind acts on the side--short of constituting heroism, pioneering health care or nursing. CONCLUSION: The article concludes with remarks on how Nightingale came to be forgotten as a health care pioneer, with comments on the two major sources that attacked her reputation, F.B. Smith in 1982, and Hugh Small in 1998. Detailed refutations in peer-reviewed sources are referenced. Finally, it is suggested that recent scandals in English hospital care, documented in the Francis Report, may provoke a revival of interest in Nightingale's principles and methods.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".