Indian Medical Students' Views on Immigration for Training and Practice
Bibliographic record
Abstract
Purpose To assess the attitudes of medical students in India about participating in graduate medical education in the United States and other countries and in subsequent clinical practice in those countries. Method A total of 240 students who were attending their final year at two medical schools in Bangalore, India, were surveyed during 2004. Surveys were completed by 166 (69%) of the students. Results Among the responding students, 98 (59%) thought of leaving India for further training abroad. Of those who wished to leave, 41 (42%) preferred the United States, 42 (43%) preferred the United Kingdom, and 9 (9%) preferred Canada, Australia or New Zealand. Only two students preferred the Middle East. Most who favored training in the United States indicated that they intended to remain after training, whereas fewer than 20% of those who favored training in the United Kingdom had such intentions. While more than 60% perceived greater professional opportunities in the United States than in India, approximately 75% were concerned that the United States had become less welcoming after the terrorist attacks of 9/11, and similar numbers were concerned about the examination administered by the Educational Commission on Foreign Medical Graduates. Conversely, the majority of respondents felt that opportunities for physicians in India were improving. Conclusions While optimism about future medical careers in India is increasing, the interest of Indian medical students in training and subsequently practicing in the United States remains high.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".