The north-south policy divide in transnational healthcare: a comparative review of policy research on medical tourism in source and destination countries
Bibliographic record
Abstract
Medical tourism occupies different spaces within national policy frameworks depending on which side of the transnational paradigm countries belong to, and how they seek to leverage it towards their developmental goals. This article draws attention to this policy divide in transnational healthcare through a comparative bibliometric review of policy research on medical tourism in select source (Canada, United States and United Kingdom) and destination countries (Mexico, India, Thailand, Malaysia and Singapore), using a systematic search of the Web of Science (WoS) database and review of grey literature. We assess cross-national differences in policy and policy research on medical tourism against contextual policy landscapes and challenges, and examine the convergence between research and policy. Our findings indicate major disparities in development agendas and national policy concerns, both between and among source and destination countries. Further, we find that research on medical tourism does not always address prevailing policy challenges, just as the policy discourse oftentimes neglects relevant policy research on the subject. Based on our review, we highlight the limited application of theoretical policy paradigms in current medical tourism research and make the case for a comparative policy research agenda for the field.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".