Conflict and costs when reforming nursing: the introduction of Nightingale nursing in Australia and Canada
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine the financial impact of Nightingale nursing in the mid-19th century; to identify any long-term implications of this financial impact on nursing. BACKGROUND: Previous research into the transformation of mid-19th century hospitals has suggested the importance of economic issues. We explore this issue from the perspective of the introduction of trained, Nightingale-style nursing. DESIGN: Historical methodology. METHODS: We use two examples, in Sydney (Australia) and Montreal (Canada), where there was a distinct break between older-style nursing and implementing of Nightingale nursing. We searched all relevant primary sources for data relating to on-going salary costs and staffing numbers. FINDINGS: We found runs of data to demonstrate the huge increase in staff numbers and salary expenses around the time of the introduction of Nightingale nursing at Sydney and Montreal General Hospitals. The one instance of declining costs was at Sydney Hospital during 1873-84. There, the salary expenditure on the bedside nurses fell as nursing probationers (students) undertook the bulk of the nursing. DISCUSSION AND CONCLUSIONS: The data available for Sydney and Montreal Hospitals supports the argument that Nightingale nursing and the demands of more effective medicine entailed a major jump in hospital costs. Given its expense, it is not surprising that conflict inevitably accompanied the introduction of Nightingale nursing. On the evidence of Sydney, a solution was found in hospital-based training schools with relatively poorly paid probationers. An on-going problem was that these schools functioned to contain nursing costs as much as to provide nursing instruction. IMPLICATIONS FOR CONTEMPORARY POLICY: While further study is needed, the two examples presented here suggest that nursing instruction in the past was a significant solution to the problem of increased hospital costs. An awareness of past solutions to on-going problems may prevent similar sacrifice of nurses in our contemporary hospital crisis.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".