The globalization of US and Canadian neurology residency training
Bibliographic record
Abstract
Neurology is becoming increasingly global, including practice, research, and education. Indeed, more than one-third of attendees at the American Academy of Neurology (AAN) annual meeting come from abroad (personal communication, AAN, September 10, 2013), and in 2012, approximately 64% of all manuscripts submitted to Neurology® came from outside the United States (personal communication, Kathy Pieper, September 9, 2013). There is a growing interest in global health electives by US and Canadian medical students and residents, who are increasingly seeking opportunities for international electives. In this issue of Neurology , Lyons et al.1 report the results of a survey to assess opportunities for global health electives in neurology residency training programs conducted by the AAN Graduate Education Subcommittee and the Member Research Subcommittee. They found that such electives are few, occurring in just over half of the responding programs; lack of funding appears to be the major obstacle in setting up such electives. Although only 61% of neurology program directors completed the survey, which may have skewed the results, 3 major themes emerged from the comments posed by the survey respondents.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".