Application of best-worst method in evaluation of medical tourism development strategy
Bibliographic record
Abstract
Medical tourism industry is an international phenomenon, which most of medical tourists for some reasons such as high costs of treatment, long waiting queues, lack of insurance and lack of access to health care in the origin country, travel long distances to benefit from health care services of destination country. Given the competitive nature of this industry, most countries are designing practical and legal services and planning for their development. For this purpose, this study has been conducted to develop a strategic planning framework for development of medical tourism industry in Yazd province of Iran; because in recent years Yazd has recognized as the health pole by patients in developing countries. In sum, emphasizing on servicing, enhancing and developing specialized treatment centers, has attracted patients from center, south and east of the country as well as Middle East and Central Asia countries. The dominant approach in this study is developmental -practical and also the research method is descriptive, analytical and survey. In order to analyzing the data, the SWOT model and best-worst techniques have been used. In the following, after identifying strategic position of Yazd province in terms of medical tourism industry, the related strategies were formulated and practical results were presented.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".