Patients beyond borders: A study of medical tourists in four countries
Bibliographic record
Abstract
This exploratory study assesses the experiences of medical travelers seeking out of country health care in four destination countries: India, China, Jordan and the United Arab Emirates. It aims to identify the source countries of medical travelers, to understand their reasons for seeking out-of-country care, the type of services they obtained, and their level of satisfaction with the experience. Cost, physician and facility reputation and hospital accreditation were ranked as the most important factors in choosing out-of-country care. Wait times at home or lack of access to care were important motivations for international medical travel. Patient assessment of treatment outcomes is as high as might be found in similar assessments in high-income country facilities. Certain forms of treatment sought by respondents (i.e. organ transplantation) raise specific ethical concerns. Also of concern is that the present health systems in all four countries fail to adequately meet the health needs of their population (notably poorer groups). Evidence and inference strongly suggest that access to health care for poorer groups will worsen in these countries as medical tourism increases, at least in the short term, raising generic ethical and policy challenges over the extent to which access to essential health care by poorer persons is compromised by the public subsidization or promotion of medical tourism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".