Exploring the influences on the Caribbean's emerging medical tourism industry
Bibliographic record
Abstract
Governments and hospitals worldwide have increasingly expressed interest in 'medical tourism', where medical treatments are privately purchased by foreign visitors seeking non-emergency care.There is steady discussion worldwide about the development of medical tourism, including countries with volumes of health service exports that are currently very small.Caribbean countries are no exception.In a region not well known for its medical tourism destinations (excepting Cuba and Costa Rica), there are regular announcements of numerous new private hospital proposals and public initiatives to create policies and incentives to support the development of the sector.While a relatively new area of research, medical tourism has received a great deal of attention in low and middle income countries that are known to attract patients from high income countries.This has resulted in a considerable body of literature that retrospectively examines its economic and health system impacts of medical tourism in 'successful' destinations, often using secondary data.This has resulted in a great deal of work examining medical tourism that has focused on limited number of countries and hospitals with established volumes of health services exports, particularly Thailand, Malaysia, and India.Little is known about the perspectives and factors that are prospectively driving interest in medical tourism worldwide, nor their attendant implications for the health systems of countries attempting to market their medical services internationally.This dissertation examines the development of the medical tourism industry in the Caribbean to prospectively explore how and why the sector is being promoted there at this time.Primarily focused on analyses of qualitative data collected from fieldwork conducted in the small island Caribbean states of Barbados and Jamaica in 2011-2013, the dissertation also incorporates semi-structured interview data from a larger study of medical tourism in Mexico, Guatemala, and Barbados from 2013-2014 to identify parallel processes and factors driving the contemporary development of medical tourism throughout the Greater Caribbean in order to better understand and articulate their health equity implications for the health systems of the region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".