Reflections on ‘medical tourism’ from the 2016 Global Healthcare Policy and Management Forum
Bibliographic record
Abstract
In October 2016, the Global Healthcare Policy and Management Forum was held at Yonsei University, Seoul, South Korea. The goal of the forum was to discuss the role of the state in regulating and supporting the development of medical tourism. Forum attendees came from 10 countries. In this short report article, we identify key lessons from the forum that can inform the direction of future scholarly engagement with medical tourism. In so doing, we reference on-going scholarly debates about this global health services practice that have appeared in multiple venues, including this very journal. Key questions for future research emerging from the forum include: who should be meaningfully involved in identifying and defining categories of those travelling across borders for health services and what risks exist if certain voices are underrepresented in such a process; who does and does not 'count' as a medical tourist and what are the implications of such quantitative assessments; why have researchers not been able to address pressing knowledge gaps regarding the health equity impacts of medical tourism; and how do national-level polices and initiatives shape the ways in which medical tourism is unfolding in specific local centres and clinics? This short report as an important time capsule that summarises the current state of medical tourism research knowledge as articulated by the thought leaders in attendance at the forum while also pushing for research growth.
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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 | 0.057 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.032 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 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".