Consumption Abroad: The Mode II of GATS (A Prefatorial Study of Medical Tourism in Gujarat State of India)
Bibliographic record
Abstract
Medical tourism is one of the major sectors amongst the trade in services that has been growing significantly the world over particularly with the establishment of General Agreement of Trade in Services (GATS) under WTO. This sector falls under Mode II - known as “Consumption Abroad” of GATS. India is the cynosure of Medical Tourists (MTs) amongst its competing countries, such as Thailand, Malaysia, Singapore, South Africa, Hong Kong, Philippines, Cuba, Hungary, Israel, Jordan, Lithuania, etc. Ahmedabad in the State of Gujarat is one of the major centres in India, in addition to Mumbai, Chennai, Bangalore, Delhi, Kolkatta, etc., where MTs have been coming for treatment in growing numbers. An attempt has been made in this paper to analyze the opinions and ratings for the quality and cost of various medical facilities availed by these MTs who had visited various hospitals in Ahmedabad for their treatment from various parts of the world e.g., US, UK, Germany, Canada, Australia, Korea, UAE, Africa, Sri Lanka, etc. In addition, the paper defines and describes the various issues related to medical tourism, such as GATS and Mode II-Consumption Abroad; Medical tourism with its historical perspective, its present scenario and reasons for medical travel; Opportunities and challenges for this sector of Indian industry and suggests the strategy i.e. entrepreneurial responsiveness and policy implications so that the Gujarat State as also the nation as a whole could get a larger pie of this growing global trade in services.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".