Bibliographic record
Abstract
The globalization of health care is often characterized as an up-and-coming phenomenon, but one aspect of the US health care system has been “globalized” for many years: internationally educated health professionals have played a significant role in the provision of health care in the United States since at least the 1960s. Today, international medical graduates (IMGs) compose approximately 25% of the nation’s physician workforce, and internationally educated nurses (IENs) compose at least 5% of the nursing workforce [1, 2]. The largest source countries of IMGs are India, the Philippines, Pakistan, and Canada [1], and the largest source countries of IENs are the Philippines, Canada, the United Kingdom, and Nigeria [2]. Although data on internationally educated dentists are more difficult to obtain, recent estimates suggest that around 8% of United States dental school graduates were originally trained overseas. The top source countries for dentists include India, the Philippines, and Colombia [3]. Given the sustained presence of internationally educated health professionals in the US health care system, it is important for state policymakers to understand the role of these professionals in North Carolina’s health care system. This article uses unpublished data from the 2008 North Carolina Health Professions Data System to examine the source-country profile and geographic distribution of the state’s internationally educated physicians, nurses, and dentists. It also discusses the role of each group in filling shortages in North Carolina and examines the broader implications of health professional migration for sending and receiving countries. Physicians IMGs composed 13.4% of the active physician workforce (2,608 of 19,449 physicians) in North Carolina in 2008—a significantly smaller proportion than the national average of 25% [1]. The largest source countries were India (23.6% of IMGs and 3.2% of all active physicians—the only country to supply more than 1% of North Carolina’s physician workforce), Canada (6.0% of IMGs), the United Kingdom (5.4% of IMGs), and the Philippines (4.4% of IMGs). The profile was similar to national statistics, although with smaller overall numbers. Also worth noting is the fact that 2.9% of North Carolina’s IMG physicians were educated in Grenada; it is likely that many of these were US citizens who were educated at offshore medical schools [4]. The geographic distribution of IMGs within North Carolina, by Area Health Education Center (AHEC) region, is shown in Figure 1. The percentage of IMGs varied from 5.6% (90 of 1,598 physicians) in the Mountain AHEC region in western North Carolina to 26.4% (310 of 1,177 physicians) in the Southern Regional AHEC region. The region with the largest number of IMGs was the Wake AHEC region in central North Carolina, with 610 IMGs (15.0% of 4,112 total physicians). Although the Area L region in northeastern North Carolina had a relatively high percentage of IMGs (21.2%), the overall number of IMGs was the smallest of any region (86 of 406 physicians). The geographic distribution of IMGs is likely influenced by visa provisions that privilege immigrant physicians willing to work in shortage areas. Since they are required to complete residency training in the United States, most IMGs enter this country on J-1 training visas, whose hold
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.076 | 0.017 |
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".