Building a New Nursing Service: Respectability and Efficiency in Victorian England
Bibliographic record
Abstract
The main problem in staffing military hospitals with female nurses, Florence Nightingale explained in 1857, was to find “respectable and efficient women” who would be willing to undertake such work. Many women would apply for the positions but few would be acceptable. “Many a woman who will make a respectable and efficient Assistant-Nurse [the equivalent of our modern staff nurse] under the eye of a vigilant Head-Nurse, will not do at all when put in a military ward,” Nightingale said, because, “As a body, the mass of Assistant-Nurses are too low in moral principle, and too flighty in manner, to make any use of.” Nightingale thought that efficient and respectable assistant nurses had “in a great degree, to be created.” Developing respectability and efficiency in hospital nurses were the two major goals of nineteenth-century nursing reformers, and vigilant supervision was to be the major method for achieving them.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".