Pediatric Care Struggles of US Trained International Medical Graduate Pediatricians in COVID-19 Pandemic
Notice bibliographique
Résumé
BACKGROUND: Pediatrician shortage and healthcare access has been a serious issue especially in medically underserved and rural areas aplenty in the USA and has further worsened during the coronavirus disease 2019 (COVID-19) pandemic. Many US trained international medical graduates (IMGs) on a visa status serve these areas to fill in the physician gap. These physicians are usually on a visa and the majority of them have approved immigration petitions. During this pandemic, the sudden changes in immigration policies in addition to the longstanding administrative backlog and processing times had posed new challenges to the pediatricians and the communities served by them. The objective of this study was to determine the demographics, level of training and practice, immigration status, the clinical role they played in the communities they served and the various professional and personal setbacks they faced during the pandemic. METHODS: A survey was created and data were collected using data collection platform "Survey Monkey". Screening questions were designed to include only IMG pediatricians on a visa status. RESULTS: A total of 267 IMG pediatricians qualified for the survey on a nationwide basis. Of the physicians that participated in the survey, 58.4% were working in either medically underserved or physician shortage areas, 36% of the total physicians were working in a rural setting, 10.6% of the pediatricians had to be quarantined due to exposure to COVID-19, 0.8% were infected with COVID-19 themselves, and 81.3% of the pediatricians had faced hindrance in being able to work at a COVID-19 hotspot due to work site restrictions because of their visa status. CONCLUSION: IMG pediatricians play a valuable role in taking care of the children in medically underserved areas. The challenges surrounding the immigration backlog are contributing to significant hardships for these pediatricians and their families and are causing a hindrance to healthcare access to the children in medically underserved communities during the pandemic especially limiting the pediatricians' scope and geographic radius of the practice, thus not allowing them to practice to the full extent of their license.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,161 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».