FOREIGN-TRAINED NURSES IN US HEALTHCARE DELIVERY
Bibliographic record
Abstract
We at CGFNS International (the Commission on Graduates of Foreign Nursing Schools, Philadelphia, Pa) noted with interest the article by Polsky et al.1 We agree with their conclusions that foreign-trained nurses are a substantial part of the US nursing workforce and that the impact of foreign-trained nurses is likely to grow in coming years. Our own findings suggest that the trend in US employment of foreign-trained nurses has continued to grow in this decade, with increasing numbers of foreign-trained nurses applying to the CGFNS VisaScreen Program, a federal screening program for nurses seeking US occupational visas. The Philippines, India, Canada, and South Korea were the primary source countries for 2003–2006 Visa Screen applications.2 The important role of foreign-trained nurses in US health care delivery has been recognized for more than a decade3–5 and is clearly rooted in market forces. Polsky et al. raised concern that the “aggressive recruitment of nurses from overseas will not be met with equally vigorous assurance of the quality and skills of the immigrating nurses.”1(p895) As Polsky et al. indicated, foreign-trained nurses are more likely than their US counterparts to have a bachelor’s degree, comparable work experience, and higher income. What was not noted in the article is that the US government has established rigorous steps for assuring that foreign nurses entering the US workforce are qualified to do so. The 1996 immigration law6 requires that all foreign nurses undergo a screening program that verifies that their education is comparable to that of a nurse educated in the United States, their nursing licenses are valid and unencumbered, they have proficiency in written and spoken English, and they have passed a test of nursing knowledge, either the CGFNS Qualifying Examination or the US licensure examination. CGFNS was named in the 1996 immigration law to conduct the screening program, and through its VisaScreen Program, protects the US public by ensuring that the credentials and nursing knowledge of foreign nurses are comparable to those of nurses educated in the United States. Although it is true that the international migration of nurses has the potential of depleting the supply of vital professionals in some poorer nations that can ill afford to lose them, the issue is complex and must be examined within the context of the nurse’s right to migrate. The international migration of nurses provides individual and family opportunity for employment, income, and security that may not be available in the countries of origin. Moreover, the return of money home to the countries of origin is significant and can be used for investment, cutting poverty, and upgrading education.7
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".