Mary Seacole and claims of evidence‐based practice and global influence
Bibliographic record
Abstract
Abstract Aim The aim of this paper was to explore the contribution of Mary Seacole to nursing and health care, notably in comparison with that of Florence Nightingale. Background Much information is available, in print and electronic, that presents Mary Seacole as a nurse, even as a pioneer nurse and leader in public health care. Her own memoir and copious primary sources, show rather than she was a businesswoman, who gave assistance during the Crimean War, mainly to officers. Florence Nightingale's role as the major founder of the nursing profession, a visionary of public health care and key player in advocating ‘environmental’ health, reflected in her ownNotes on Nursing, is ignored or misconstrued. Design Discussion paper. Data sources British newspapers of 19th century andThe Timesdigital archive; Australian and New Zealand newspaper archives, published memoirs, letters and biographies/autobiographies of Crimean War participants were the major sources. Results Careful examination of primary sources, notably digitized newspaper sources, British, Australian and New Zealand, show that the claims for Seacole's ‘global influence’ in nursing do not hold, while her use of ‘practice‐based evidence’ might better be called self‐assessment. Primary sources, moreover, show substantial evidence of Nightingale's contributions to nursing and health care, in Australia, New Zealand, theUSAand many countries and theUKmuch material shows her influence also on hospital safety and health promotion.
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 | 0.193 | 0.339 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.094 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.012 | 0.016 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".