Enriching health-professional programs in global health: Development and implementation of an interdisciplinary and integrated approach
Bibliographic record
Abstract
BACKGROUND: Globalization results in a rapidly diversifying population, increased inequities, and more complex health problems affecting populations. This forces medical schools to integrate global health (GH) into the training of health-care professionals from curriculum development to practical learning activities, here and abroad. APPROACH: The approach aims at enriching existing programs in GH competencies in an interdisciplinary context. The goal is to ensure that all health-science students develop a certain level of GH competency. The main actions are the mobilization of key stakeholders, the development of a competency framework (CF) to perform gap analysis, tool formalization, and monitoring and evaluation activities. Subsequent to scoping review and stakeholder consultations, ten principles are identified and used to guide the enrichment process. RESULTS: Actual outputs cover a broad scope, from key decision-makers' support and endorsement to the formalization of tools and the consolidation and creation of activities such as service-learning activities, rotations among underserved populations, and training for international rotations. CONCLUSION: While this unique approach is proving to be a major challenge, the preliminary results are well worth the effort. The project's tangible impacts on health-sciences teaching, the GH competence of graduates, and care delivery are topics of interest for future investigation.
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 | 0.090 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".