Bibliographic record
Abstract
Objective: Nursing is as old as mankind and the nature of what it means to be a man in nursing has a wide and varied history. Men have been at the forefront of nursing practice from before the birth of Christ – the first record of male nursing originates from ancient India. Slowly over time the image of the male nurse has given way to the dominance of women largely thanks to Florence Nightingale. The aim of this paper is to discuss the contribution men have made to the profession of nursing through the early years of nursing’s history in particular from 250BC to the early 1900’s.Methods and result: Design: A historical review. Data Sources: The search strategy included research studies both qualitatively and quantitatively, as well as anecdotal and discursive evidence from 1900-2015. Implications for Nursing: The predominance of the history of has always had a focus on the female perspective. Men have had played a significant part in the development of that history. Acknowledging the role men have contributed in developing and promoting nursing practice is equally as valid and as such should be recognised accordingly.Conclusions: Male nursing has had a varied history from the first recoded nursing school in 256BC to its slow eventual slow demise from the 1840’s. Records reveal the work of the male nurse was seen predominately within secular institutions and personified aspects of care that focused totally on patient wellbeing both physically and spiritually.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".