Challenges of Medical Tourism: Managerial Paradigm in the Indian Framework
Bibliographic record
Abstract
Abstract ................................................................................................. 373 19.1 Introduction .................................................................................. 374 19.2 New Paradigms in Healthcare Business ...................................... 384 19.3Future of Medical Tourism-Challenges Faced: An Indian Perspective ................................................................................... 388 19.4 Building Professional Competency and a Better Healthcare Management ................................................................................. 395 19.5 Conclusion ................................................................................... 397 Keywords .............................................................................................. 397 References ............................................................................................. 398 ABSTRACT Medical tourism is multifaceted in nature as the versatility offers many variants to different segments that come from diverse backgrounds-India to mull over for value creation in a networked healthcare environment and also building professional competency through healthcare managers. There is an enormous potential for Indian healthcare system to reach out, Christ (Deemed to be University), Bangalore, India with its services, beyond frontiers. The purpose of the study necessitates the role of the hospitality sector in promoting medical tourism in coordination with the hospital sector. Considering all these factors, there is an imperative need to undertake the present study of the various independent variables impacting the growth of medical tourism in south India. This is derived from the differing socioeconomic determinants, tourist arrival from different geopolitical regions; and demand generated. The general perception is that outbound medical tourism in the United States, the United Kingdom, Canada, and other Western countries is on a rise, and a lot of medical tourists are arriving in certain developing countries such as India, Thailand, Jordan, and Singapore for medical procedures that are cost effective. The dynamics of medical tourism, however, is much more intriguing beyond a naive representation. The research was undertaken keeping in mind the need for the study as mentioned below.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".