Bibliographic record
Abstract
In 2010, the government of the United Arab Emirates (UAE) spent a quarter of its total healthcare budget to send its citizens abroad for medical treatment. These patients, consumers who cross international borders for the purpose of obtaining healthcare, are participants in a phase of globalization referred to as “medical travel” or “medical tourism.” Their movement coincides with the cross-border flow of health services, professionals, and companies, shaping a global industry valued at as much as U.S. $55 billion. In the years ahead, this industry is expected to grow—and, in doing so, to bring a greater number of national health systems in contact with international patients and providers. Bearing witness to these changes, the UAE has increasingly looked to medical travel—and attracting international patients—to improve its health system and to diversify its economy. These outcomes, however, overshadow the equity effects that may result from the influx of such patients, potentially crowding out local residents, especially expatriates, who may see little from these gains. This working paper provides evidence, based on the examples of Dubai and Ras Al Khaimah, that medical travel presents the UAE with a mix of equity benefits and harms. To manage these harms, the paper recommends that local governments and healthcare providers incorporate monitoring and planning mechanisms into their medical travel initiatives.
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".