Notice bibliographique
Résumé
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 This article uses unpublished data from the 2008 North Carolina Health Professions Data System to examine the source-country profile and geographic distribution of the state’s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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,008 | 0,008 |
| Communication savante | 0,021 | 0,024 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,076 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».