Bidirectional Global Health Education: The RVCP-Jeff HEALTH Exchange Program
Bibliographic record
Abstract
MattersPopulation Health School of Population Health In 2005, Thomas Jefferson University (TJU) medical students and faculty from the Department of Family and Community Medicine (DFCM) started the Rwanda Health and Healing Project, a community oriented health project in two rural villages in Rwanda.This project has been previously described in the Population Health Matters article, "Partnerships for Health in Rwandan Genocide Survivors Village: The Rwanda Health and Healing Project and Barefoot Artists."1Since its inception, over 80 students and faculty from TJU have traveled to Rwanda to work with these villages on a variety of public health and income generating projects.2As part of this program, a partnership was formed with the Rwanda Village Concept Project (RVCP), a Rwandan medical student-driven public health and community development organization.In 2007, a group of dedicated Jefferson students from the student organization Jeff HEALTH worked with faculty from the DFCM to establish an exchange program to bring RVCP medical students to Jefferson.Through the Jeff HEALTH-RVCP partnership, Jefferson selects 2-3 Rwandan students per year (through a rigorous essay and interview process) for 2-month long TJU rotations focused on primary care, community health, and public health.Since 2007, 21 Rwandan students have successfully completed this exchange program.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 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".