Historically‐informed nursing: A transnational case study in China
Bibliographic record
Abstract
The term 'nurse' (hushi-'caring scholar') did not enter the Chinese language until the early 20th century. Modern nursing-a fundamentally Western notion popularized by Nightingale and introduced to China in 1884-profoundly changed the way care of the sick was practiced. For 65 years, until 1949, nursing developed in China as a transnational project, with Western and Chinese influences shaping the profession of nursing in ways that linger today. Co-authored by Chinese, Canadian, and American nurses, this paper examines the early stages of nursing in one province of China as an exemplar of the transnational nature of nursing development. By identifying sociopolitical influences on the early development of nursing in Shandong, the authors aimed not only to contribute to the nascent body of knowledge on China nursing history, but also to heighten readers' sensitivity to the existence of historical echoes, residue, and resonances in their own nursing practices. Tracing current issues, values, or practices back to their roots provides context and helps us to better understand the present. Whether we are aware of the details or not, the gestalt of nursing practice in a particular place has been shaped by its history-including in Shandong province in China.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 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".