“That’s enough patients for everyone!”: Local stakeholders’ views on attracting patients into Barbados and Guatemala’s emerging medical tourism sectors
Bibliographic record
Abstract
BACKGROUND: Medical tourism has attracted considerable interest within the Latin American and Caribbean (LAC) region. Governments in the region tout the economic potential of treating foreign patients while several new private hospitals primarily target international patients. This analysis explores the perspectives of a range of medical tourism sector stakeholders in two LAC countries, Guatemala and Barbados, which are beginning to develop their medical tourism sectors. These perspectives provide insights into how beliefs about international patients are shaping the expanding regional interest in medical tourism. METHODS: Structured around the comparative case study methodology, semi-structured interviews were conducted with 50 medical tourism stakeholders in each of Guatemala and Barbados (n = 100). To capture a comprehensive range of perspectives, stakeholders were recruited to represent civil society (n = 5/country), health human resources (n = 15/country), public health care and tourism sectors (n = 15/country), and private health care and tourism sectors (n = 15/country). Interviews were transcribed verbatim, coded using a collaborative process of scheme development, and analyzed thematically following an iterative process of data review. RESULTS: Many Guatemalan stakeholders identified the Guatemalan-American diaspora as a significant source of existing international patients. Similarly, Barbadian participants identified their large recreational tourism sector as creating a ready source of foreign patients with existing ties to the country. While both Barbadian and Guatemalan medical tourism proponents share a common understanding that intra-regional patients are an existing supply of international patients that should be further developed, the dominant perception driving interest in medical tourism is the proximity of the American health care market. In the short term, this supplies a vision of a large number of Americans lacking adequate health insurance willing to travel for care, while in the long term, the Affordable Care Act is seen to be an enormous potential driver of future medical tourism as it is believed that private insurers will seek to control costs by outsourcing care to providers abroad. CONCLUSIONS: Each country has some comparative advantage in medical tourism. Assumptions about a large North American patient base, however, are not supported by reliable evidence. Pursuing this market could incur costs borne by patients in their public health systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".