Medical tourism
Bibliographic record
Abstract
Medical tourism refers to the movement of patients to countries outside their country of residence for medical reasons and access to high-quality services at lower costs. The causes of this phenomenon include economic differences in services between countries, shorter waiting times, and the modern technology that characterizes the healthcare systems of these countries. Aim: The aim of this thesis is to focus on the theoretical approach to the phenomenon of medical tourism, examining holistically its key aspects, advantages, disadvantages, its major dimensions in the global market, the countries promoting it, and the relevant legislation, without concentrating exclusively on specific details The benefits of medical tourism include advantages for the provider countries, such as boosting the economy and creating new job opportunities, as well as additional benefits for patients who benefit from affordable prices and higher-quality medical services. However, there are also disadvantages, such as the significant pressures on healthcare systems, the high expenses needed for the smooth operation of the sector, and the varying legislation that applies in the countries offering it. Some of the key countries involved in medical tourism are India, Thailand, Canada, Germany, and Turkey. The competition between countries concerns the prices and quality of services they offer, with the goal of attracting more patients. Method and materials: The methodology followed for the development of this thesis is a literature review of scientific journals, textbooks, books, and the internet, with the aim of providing the most accurate depiction of the phenomenon of medical tourism based on real-world data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.228 | 0.086 |
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".