Training medical students in human rights: a fifteen-year experience in Geneva
Bibliographic record
Abstract
BACKGROUND: Training health professionals in the field of human rights has long been advocated by the United Nations. Over the past decade some medical schools have introduced health and human rights courses, yet by far not all. This paper describes the objectives and the content of the Health and Human Rights program developed at the Faculty of Medicine, University of Geneva. METHODS: The health and human rights program was developed through the identification of the course objectives, contents, and educational modalities using consensus techniques, and through a step by step implementation procedure integrating multiple evaluation processes. RESULTS: Defined objectives included the familiarization with the concepts, instruments and mechanisms of human rights, the links between health and human rights, and the role of health professionals in promoting human rights. The content ultimately adopted focused on the typology of human rights, their mechanisms of protection, their instruments, as well as social inequalities and vulnerable groups of the population. The implementation proceeded through a step by step approach. Evaluation showed high satisfaction of students, good achievement of learning objectives, and some academic and community impact. CONCLUSIONS: High interest of students for a human rights course is encouraging. Furthermore, the community projects initiated and implemented by students may contribute to the social responsibility of the academic institution.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".