Identifying the Research Extent of Medical Tourism in the World and the Components of Attracting Medical Tourists in Iran
Bibliographic record
Abstract
Introduction: Health tourism is an organized trip from one's living environment to another place, which is done in order to maintain, improve and regain physical and mental health. The purpose of this research was to identify the most important factors affecting medical tourism in Iran. Methods: This was a fundamental study that was conducted in two phases. The first phase consisted of two steps. The first step was to perform a systematic review with the keyword of "MEDICAL TOURISM" on Web of Science and select 434 articles, and the second step was to perform a scientometric analysis on these articles using VOSviewer software. The second phase consisted of three steps. The first step was a systematic review with some keywords, including "MEDICAL TOURISM" and "medical tourism" on foreign and domestic databases and selecting 63 articles. The second step was to perform a content analysis on the selected articles using Nvivo software and identify the components affecting medical tourism in the world. The third step was the implementation of a Delphi method using a Likert scale with the aim of identifying the most important factors affecting medical tourism in Iran from the point of view of academic experts. Results: The first phase showed that the scope of research on the topic of medical tourism in the world had an upward trend from 1975 to 2020, and the countries of America, Canada, Malaysia, South Korea and Iran were the top 5 countries in this field. In the second phase, 46 factors affecting medical tourism in the world were identified, and after summarizing the opinions of experts, 39 indicators were determined as indicators affecting medical tourism in Iran. Conclusion: Considering the high number and variety of factors affecting medical tourism in Iran, the development of this industry in the country requires extensive intra- and inter-sectorial coordination.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".