Nurses Across Borders: Foregrounding International Migration in Nursing History
Bibliographic record
Abstract
Although the international migration of nurses has played a formative role in increasing the racial and ethnic diversity of the health care labor force, nursing historians have paid very little attention to the theme of international migration and the experiences of foreign-trained nurses, A focus on international migration complements two new approaches in nursing history: the agenda to internationalize its frameworks, and the call to move away from "great women, great events" and toward the experiences of "ordinary" nurses. This article undertakes a close reading of the life and work of Filipino American nurse Ines Cayaban to reconceptualize nursing biography in an international framework that is attentive to issues of migration, race, gender, and colonialism. It was a Hannah keynote lecture delivered by the author on June 5, 2008, as part of the CAHN/ACHN (Canadian Association for the History of Nursing/Association Canadienne pour l'Histoire du Nursing) International Nursing History Conference.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".