Medical professionalism in the new millennium: are there intercultural differences? Brief report and commentary.
Bibliographic record
Abstract
We hypothesized that differences in premedical and medical indoctrination might lead to demonstrable differences in notions of medical professionalism among U.S. medical schoolgraduates (USMG) and international medical graduates (IMG). We used the previously validated Barry Challenges to Professionalism questionnaire to query applicants to our Medicine residency. Two hundred sixty-six of 1,476 applicants responded; 57 were USMG and 188 IMG were non-U.S. citizens. There were no significant differences in responses based on gender or medical school background (comparing USMG vs IMG). Graduates of U.S. and Canadian schools were more likely than those of Indian schools to answer correctly three of 10 questions. We use the results of this ostensibly "negative" study to comment on the foundations for the hypothesis and logistic difficulty of studying the question.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".