Residency and Fellowship Training Programs in the United States of America
Bibliographic record
Abstract
Background: International medical graduates (IMGs) who study abroad face multiple challenges and more significant discrimination compared to that experienced by other graduates. These obstacles take different forms and occur in multiple stages. Furthermore, adaptation to a new culture causes several challenges for them, affecting their training and patient care. Objectives: This study was done to evaluate the personal experiences of Saudi IMGs and to describe the challenges they encounter during their residency and fellowship training programs in the United States of America. Materials and Methods: A cross-sectional online survey was conducted online in 2013. Participants included 230 Saudi IMGs enrolled in residency or fellowship training programs in the United States of America. Results: The majority of the respondents were males and strongly disagreed that lack of English language proficiency was a barrier to learning. High disagreement on discriminatory criticism was most common in postgraduate year 5 (R5) and higher levels (44.8%). Most participants reported positive experiences involving the learning environment. Moreover, some participants reported that they did not find it difficult to perform their religious activities. Total 43.4% of the participants reported equality of treatment regarding administrative responsibilities. However, subgroup analysis showed that women's experiences were less favorable than those observed in the male population. Conclusions: Results suggested that Saudi IMGs had an overall positive experience and faced minor barriers while studying in the United States of America. However, subgroup analysis showed that women's experiences were less positive relative to men.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".