Web-based medical facilitators in medical tourism: the third party in decision-making
Bibliographic record
Abstract
The emergence of web-based medical tourism facilitators (MTFs) has added a new dimension to the phenomenon of cross-border travel. These facilitators are crucial connectors between foreign patients and host countries. They help patients navigate countries, doctors and specialties. However, little attention has been paid to the authenticity of information displayed on the facilitators' web portals, and whether they follow ethical guidelines and standards. This paper analyses the available information on MTF portals from an ethics perspective. It compares 208 facilitators across 47 countries for the services offered. Data were collected from the databases of the Medical Tourism Association and World Medical Resources. India was the most common destination country linked to 81 facilitators. The five countries with the maximum number of facilitators were the USA, the UK, India, Canada and Poland. This paper identifies concerns regarding the information displayed about patients' safety, and the maintenance of confidentiality. There is a need to develop ethical standards for this field.
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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 | 0.050 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.005 | 0.029 |
| Insufficient payload (model declined to judge) | 0.017 | 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; both teacher heads agree on what is shown here.
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".