Pediatric Care Struggles of US Trained International Medical Graduate Pediatricians in COVID-19 Pandemic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; both teacher heads agree on what is shown here.
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".