Development Strategies for Taking Thailand’s Health Healing Tourism Business into the Global Market
Bibliographic record
Abstract
As a business, medical tourism seems to offer a wide range of products/services to potential customers, especially those from overseas. In this regard, the Thai government has adopted a policy to promote Thailand, as the medical hub of Asia, capable of providing world-class health care. The purpose of this study, therefore, is to analyze the development strategy of the medical tourism business using a combination of methods, both qualitative and quantitative in nature, to achieve this objective. The sample for this study consisted of 310 foreigners undergoing medical treatment in private hospitals in Bangkok, and the data collected from these respondents is presented in statistical form through the use of Path Analysis. The strategies pertaining to the development of medical tourism consist of demand strategies, supply strategies, research framework strategies (such as the perceived potential of the medical tourism industry for providing quality service), appeal, management of relevant public services, and service quality strategies, including those relating to strong and weak points. Furthermore, using path analysis to determine the relationship between public management, the quality of the hospital’s service, and the appeal of the country’s amenities that affects foreigners’ perception of the country’s potential regarding health facilities. It was found that the most important factor was the quality of the service provided, while the second most influential variable was appeal.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".