Providers’ perspectives on inbound medical tourism in Central America and the Caribbean: factors driving and inhibiting sector development and their health equity implications
Bibliographic record
Abstract
BACKGROUND: Many governments and health care providers worldwide are enthusiastic to develop medical tourism as a service export. Despite the popularity of this policy uptake, there is relatively little known about the specific local factors prospectively motivating and informing development of this sector. OBJECTIVE: To identify common social, economic, and health system factors shaping the development of medical tourism in three Central American and Caribbean countries and their health equity implications. DESIGN: In-depth, semi-structured interviews were conducted in Mexico, Guatemala, and Barbados with 150 health system stakeholders. Participants were recruited from private and public sectors working in various fields: trade and economic development, health services delivery, training and administration, and civil society. Transcribed interviews were coded using qualitative data management software, and thematic analysis was used to identify cross-cutting issues regarding the drivers and inhibitors of medical tourism development. RESULTS: Four common drivers of medical tourism development were identified: 1) unused capacity in existing private hospitals, 2) international portability of health insurance, vis-a-vis international hospital accreditation, 3) internationally trained physicians as both marketable assets and industry entrepreneurs, and 4) promotion of medical tourism by public export development corporations. Three common inhibitors for the development of the sector were also identified: 1) the high expense of market entry, 2) poor sector-wide planning, and 3) structural socio-economic issues such as insecurity or relatively high business costs and financial risks. CONCLUSION: There are shared factors shaping the development of medical tourism in Central America and the Caribbean that help explain why it is being pursued by many hospitals and governments in the region. Development of the sector is primarily being driven by public investment promotion agencies and the private health sector seeking economic benefits with limited consideration and planning for the health equity concerns medical tourism raises.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".