Medical Tourism's Impact on Health Care Equity and Access in Low-and-Middle-Income Countries: Making the Case for Regulation
Bibliographic record
Abstract
There is currently an evidentiary gap in the scholarship concerning medical tourism’s impact on low-and-middle-income destination countries (LMICs). This article reviews relevant evidence that exists, and concludes that there are signs of correlation between medical tourism and the expansion of private, technology-intensive health care in LMICs, which has largely remained out of reach for the majority of the local patients. In light of this health care inequity between local residents and medical tourists in LMICs, we argue that the presumption should not be in favor of medical tourism, and that LMIC governments have a legitimate interest in seeking to regulate the medical tourism industry to ensure the net effect for their citizens is positive. Moreover, sending countries, particularly those in the developed world, have the responsibility to adopt public policies to diminish their citizens’ demand for medical tourism, and to work with LMICs to ensure that the growth of medical tourism does not occur at the expense of the poorest of the poor.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".