Not Above the Law: A Legal and Ethical Analysis of Short-Term Experiences in Global Health
Bibliographic record
Abstract
BACKGROUND: Persons from high-income countries have multiple opportunities today to participate in "short-term experiences in global health" (STEGHs) in low-resourced countries. STEGHs are organized through religious missions, service learning, medical internships, global health education, and international electives. An issue of increasing concern in STEGHs is "hands-on" participation in clinical procedures by volunteers and students with limited or no medical training. To address these concerns, best practices and ethical standards have been developed. However, not all STEGH organizations adhere to these guidelines, and some actively or tacitly allow unethical and potentially illegal practices. OBJECTIVES: This paper considers the legal framework within which STEGHs operate. It assesses whether certain STEGH practices break laws in the US and/or host countries or violate international "soft" legal norms. Two activities of particular concern are: practicing medicine without a license and drug importation and distribution. CONCLUSIONS: Many activities undertaken in STEGHs would be illegal if they took place on US soil. In addition, these same activities are often illegal in the host countries where STEGHs operate, although compliance is unevenly enforced. Many STEGH activities violate World Health Organization guidelines for ethical conduct in humanitarian activities. RECOMMENDATIONS: This paper encourages STEGH organizations to end unethical and potentially illegal activities; urges regulatory and non-regulatory stakeholders to alter policies that motivate participation in illegal or unethical STEGH activities; and encourages host countries to enforce their local and national health laws.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".