Medical tourism's impacts on health worker migration in the Caribbean: five examples and their implications for global justice
Bibliographic record
Abstract
Medical tourism is a practice where individuals cross international borders in order to access medical care. This practice can impact the global distribution of health workers by potentially reducing the emigration of health workers from destination countries for medical tourists and affecting the internal distribution of these workers. Little has been said, however, about the impacts of medical tourism on the immigration of health workers to medical tourism destinations. We discuss five patterns of medical tourism-driven health worker migration to medical tourism destinations: 1) long-term international migration; 2) long-term diasporic migration; 3) long-term migration and 'black sheep'; 4) short-term migration via time share; and 5) short-term migration via patient-provider dyad. These patterns of health worker migration have repercussions for global justice that include potential negative impacts on the following: 1) health worker training; 2) health worker distributions; 3) local provision of care; and 4) local economies. In order to address these potential negative impacts, policy makers in destination countries should work to ensure that changes in health worker training and licensure aimed at promoting the medical tourism sector are also supportive of the health needs of the domestic population. Policy makers in both source and destination countries should be aware of the effects of medical tourism on health worker flows both into and out of medical tourism destinations and work to ensure that the potential harms of these worker flows to both groups are mitigated.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".