Bioethics and transnational medical travel: India, “medical tourism”, and the globalisation of healthcare
Bibliographic record
Abstract
Health-related travel, also referred to as "medical tourism" is historically well-known. Its emerging contemporary form suggests the development of a form of globalised for-profit healthcare. Medical tourism to India, the focus of a recent conference in Canada, provides an example of the globalisation of healthcare. By positioning itself as a low-cost, high-tech, fast-access and high-quality healthcare destination country, India offers healthcare to medical travellers who are frustrated with waiting lists and the limited availability of some procedures in Canada. Although patients have the right to travel and seek care at international medical facilities, there are a number of dimensions of medical tourism that are disturbing. The diversion of public investments in healthcare to the private sector, in order to serve medical travellers, perversely transfers public resources to international patients at a time when the Indian public healthcare system fails to provide primary healthcare to its own citizens. Further, little is known about patient safety and quality care in transnational medical travel. Countries that are departure points as well as destination countries need to carefully explore the ethical, social, cultural, and economic consequences of the growing phenomenon of for-profit international medical travel.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| 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".