BEYOND SUN, SAND, AND STITCHES: ASSIGNING RESPONSIBILITY FOR THE HARMS OF MEDICAL TOURISM
Bibliographic record
Abstract
Medical tourism (MT) can be conceptualized as the intentional pursuit of non-emergency surgical interventions by patients outside their nation of residence. Despite increasing popular interest in MT, the ethical issues associated with the practice have thus far been under-examined. MT has been associated with a range of both positive and negative effects for medical tourists’ home and host countries, and for the medical tourists themselves. Absent from previous explorations of MT is a clear argument of how responsibility for the harms of this practice should be assigned. This paper addresses this gap by describing both backward looking liability and forward looking political responsibility for stakeholders in MT. We use a political responsibility model to develop a decision-making process for individual medical tourists and conclude that more information on the effects of MT must be developed to help patients engage in ethical MT.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| 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".