Strengthening Mentorship in Global Health for US Medical Students
Bibliographic record
Abstract
US medical students demonstrate strong interest in receiving global health training. In 2012, the Center for Global Health (CGH) at the University of Illinois College of Medicine (UICOM) developed a Global Medicine (GMED) program to match this interest. From its initiation, mentorship has been a key component of the GMED program. More recently, this has been strengthened by applying additional evidence-informed approaches toward mentoring. These include the "mentor up" approach, a "network of mentors," and an individualized development plan (IDP). Applying these changes were associated with increases in the number of student abstract presentations and peer-reviewed journal publications. Mentorship based upon evidence-informed approaches should be a key component of global health education in academic medical centers.
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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 | 0.053 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".