Lynn McDonald, <i>Florence Nightingale and the Medical Men: Working Together for Health Care Reform</i>
Bibliographic record
Abstract
Lynn McDonald is a Canadian former politician and social scientist who has spent some 30 years developing and promoting understanding of Florence Nightingale—mainly via the laborious method of transcribing, compiling and contextualising her enormous archive. Since completing the defining achievement of this endeavour, the annotated, 16-volume Collected Works of Florence Nightingale (2001–2012), McDonald has published several tangential shorter books, of which this, on Nightingale’s relationship with the medical profession, is to be the last. Extensively cross-referenced to the Collected Works, Florence Nightingale and the Medical Men also utilises numerous fresh primary sources, notably contemporary publications and letters Nightingale received from doctors. It is a valuable, if patchy and occasionally frustrating, work. The book proceeds with McDonald’s characteristic thoroughness, setting out to give details and references for every significant relationship Nightingale had with medical figures throughout her life. These are grouped into categories, beginning, after an introductory chapter summarising the state of medicine, nursing and public health in the mid-nineteenth century, with the Crimean War. Most of the Crimea material, explaining Nightingale’s strained relationships with army medical leaders, will be familiar to scholars, though McDonald’s summaries of the various commissions sent by the British government to improve matters, and comparisons with conditions in the French army, are clear and useful.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".