International Electives in Neurology Training: A Survey of U.S. and Canadian Program Directors (P4.301)
Bibliographic record
Abstract
OBJECTIVE:To ascertain the current status of global health training and humanitarian relief opportunities in United States and Canadian postgraduate neurology programs. BACKGROUND:There is a growing interest among North American trainees to pursue medical electives in low- and middle-income countries. Such training opportunities provide many educational and humanitarian benefits, but also pose several challenges related to organization, human resources, funding, and trainee and patient safety. The current support and engagement of neurology postgraduate training programs for trainees to pursue international rotations is unknown. DESIGN/METHODS:A survey was distributed to all program directors in the United States and Canada (Dec. 2012-Feb. 2013) through the American Academy of Neurology to assess the training opportunities, institutional partnerships, and support available for international neurology electives. RESULTS:Approximately half of responding programs (53%) allow residents to pursue global health related electives, and 11% reported that at least one trainee participated in humanitarian relief during training (survey response rate 61%, 143/234 program directors). The number of trainees participating in international electives was low: 0-9% of residents (55% of programs) and 10-19% of residents (21% of programs). Lack of funding was the most commonly cited reason for residents not participating in global health electives. If funding was available, 93% of program directors stated there would be time for residents to participate. CONCLUSIONS:75% of program directors are interested in further information on global health electives. However, currently only half of US and Canadian neurology training programs include international electives, mostly due to a reported lack of funding. The number of North American neurology trainees venturing abroad remains a minority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".