Bibliographic record
Abstract
Medical tourism is a term involving people who travel to a different place to receive treatment for a disease, ailment, or condition, and who are seeking lower cost of care, higher quality of care, better access to care, or different care than they could receive at home. The goal of health care associations is often to raise awareness of medical tourism in the hopes of expanding the industry. The Medical Tourism Association (MTA) is a non-profit trade association. It is made up of international hospitals, healthcare providers, medical travel facilitators, insurance companies, and other affiliates. Factors that have led to the increasing popularity of medical travel include the high cost of health care, long wait times for certain procedures, the ease and affordability of international travel, and improvements in both technology and standards of care in many countries. Medical tourism comes from a variety of locations including Europe, the Middle East, Japan, the United States and Canada. International healthcare accreditation organizations certify a wide range of healthcare programs such as hospitals, primary care centers, medical transport, and ambulatory care services. The cost of surgery in India, Thailand or South Africa can be one-tenth of what it is in the United States or Western Europe, and sometimes even less. There are various categories of treatments get through Indian medical tourism agency and these treatments include Kidney Transplant surgery, Bone marrow transplant surgery, Hip Replacement surgery, Knee Replacement surgery, Ayurveda Treatment and Liver Transplant surgery. The present study analyses the growth of medical tourism in India. The data for the study has been collected through secondary source such as general reports, books, journals and web sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".