“The Major Forces that Need to Back Medical Tourism Were … in Alignment”
Bibliographic record
Abstract
Governments around the world have expressed interest in developing local medical tourism sectors, framing the industry as an opportunity for economic growth and health system improvement. This article addresses questions about how the desire to develop a medical tourism sector in a country emerges and which stakeholders are involved in both creating momentum and informing its progress. Presenting a thematic analysis of 19 key informant interviews conducted with domestic and international stakeholders in Barbados's medical tourism sector in 2011, we examine the roles that "actors" and "champions" at home and abroad have played in the sector's development. Physicians and the Barbadian government, along with international investors, the Medical Tourism Association, and development agencies, have promoted the industry, while actors such as medical tourists and international hospital accreditation companies are passively framing the terms of how medical tourism is unfolding in Barbados. Within this context, we seek to better understand the roles and relationships of various actors and champions implicated in the development of medical tourism in order to provide a more nuanced understanding of how the sector is emerging in Barbados and elsewhere and how its development might impact equitable health system development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".