STEM CELL TOURISM AND FUTURE STEM CELL TOURISTS: POLICY AND ETHICAL IMPLICATIONS
Bibliographic record
Abstract
Stem cell tourism is a small but growing part of the thriving global medical tourism marketplace. Much stem cell research remains at the experimental stage, with clinical trials still uncommon. However, there are over 700 clinics estimated to be operating in mostly developing countries--from Costa Rica and Argentina to China, India and Russia--that have lured many patients, mostly from industrialized countries, driven by desperation and hope, which in turn continue to fuel the growth of such tourism. While much research has focused on such dimensions as the promotions that allow such businesses to make their services known, media coverage, some patient research, and regulatory conditions for developing country clinics, little attention has been paid to the non-affected members of the general population, the future potential users of such services. This empirical study based on five focus group discussions with a diverse group of healthy adults in a Canadian city, explored participant views of patients who use stem cell tourism services, the likelihood they would avail themselves of such services if they were to suffer similar illnesses, and the conditions under which they might do so, and the impact that admonitions and advice from international expert bodies might have on their decisions. Our findings suggest that these healthy adults are sympathetic to the drivers of hope and desperation, and, despite cautions about research limitations, may seek such treatments themselves under similar conditions. These findings are discussed in the context of the policy and ethical issues raised by this form of medical tourism.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".