A Comprehensive Framework to Optimize Short-Term Experiences in Global Health (STEGH)
Bibliographic record
Abstract
Increasing demand for Short-term Experiences in Global Health (STEGH), particularly among medical trainees, has seen a growth in programming that brings participants from high-income countries to low and middle-income settings in order to engage in service, teaching or research activities. Historically the domain of faith-based organizations conducting "missions", STEGH are now offered by diverse groups including academic institutions, non-profit organizations, and the private sector, either as dedicated for-profits or through corporate social responsibility arms.The growing popularity of STEGH has resulted in concerns about their negative impacts on host communities. Traditional STEGH are often crafted with little or no input from host community leaders, and this results in activities that do not address locally identified priorities. Other concerns include culturally incongruent programming and the creation of parallel systems that disrupt established local services and redirect scarce local resources, which fosters dependency instead of building capacity. One concern specific to trainees also includes trainee provision of services beyond their scope and training level.To address these concerns, this paper presents a comprehensive framework that aims to categorize promising interventions that might promote greater responsibility in STEGH. Based on the micro-meso-macro framework, this paper proposes various interventions as incentives and disincentives to be deployed at the individual, program, and societal levels to promote greater responsibility in STEGH. Deployed altogether, the interventions contemplated by this framework would foster the optimal context required to encourage responsibility, minimize harms, and optimize host community outcomes for STEGH.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".