Are Chinese nurses a viable source to relieve U.S. nursing shortage?
Bibliographic record
Abstract
Nurse shortage has become a global issue. According to the International Council of Nurses (ICN), the majority of member states of the World Health Organization have reported some degree of nurse shortage (ICN, 2002). The shortage has been more pronounced in developed countries. Although international recruitment of nurses has been going on for decades, it has intensified in the last few years, particularly in Britain, Canada, and the United States. Nurse shortage in the United States is a cyclic phenomenon. However, the current shortage has taken place in different national and international contexts and is unlikely to be resolved within a foreseeable period of time (Buerhaus, Steiger, & Auerbach, 2000).\nIn this article, international nurse migration to the United States is examined in the global context through the push-pull theoretical framework. In particular, the viability of recruiting Chinese nurses to ease the current U.S. nurse shortage is explored by assessing their socioeconomic conditions and educational preparation. It is not only possible, but also feasible, for U.S. health care providers to recruit Chinese nurses. Providing the opportunity to work in the Untied States is a win-win scenario to individual Chinese nurses, the Chinese nursing profession, China, and United States.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".