Fly-By medical care: Conceptualizing the global and local social responsibilities of medical tourists and physician voluntourists
Bibliographic record
Abstract
BACKGROUND: Medical tourism is a global health practice where patients travel abroad to receive health care. Voluntourism is a practice where physicians travel abroad to deliver health care. Both of these practices often entail travel from high income to low and middle income countries and both have been associated with possible negative impacts. In this paper, we explore the social responsibilities of medical tourists and voluntourists to identify commonalities and distinctions that can be used to develop a wider understanding of social responsibility in global health care practices. DISCUSSION: Social responsibility is a responsibility to promote the welfare of the communities to which one belongs or with which one interacts. Physicians stress their social responsibility to care for the welfare of their patients and their domestic communities. When physicians choose to travel to another county to provide medical care, this social responsibility is expanded to this new community. Patients too have a social responsibility to use their community's health resources efficiently and to promote the health of their community. When these patients choose to go abroad to receive medical care, this social responsibility applies to the new community as well. While voluntourists and medical tourists both see the scope of their social responsibilities expand by engaging in these global practices, the social responsibilities of physician voluntourists are much better defined than those of medical tourists. Guidelines for engaging in ethical voluntourism and training for voluntourists still need better development, but medical tourism as a practice should follow the lead of voluntourism by developing clearer norms for ethical medical tourism. SUMMARY: Much can be learned by examining the social responsibilities of medical tourists and voluntourists when they engage in global health practices. While each group needs better guidance for engaging in responsible forms of these practices, patients are at a disadvantage in understanding the effects of medical tourism and organizing responses to these impacts. Members of the medical professions and the medical tourism industry must take responsibility for providing better guidance for medical tourists.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".