Sağlık turizmi konulu yayınların bilim haritalama yöntemiyle analizi
Bibliographic record
Abstract
Introduction and objective: Health tourism, which is defined as the act of traveling abroad to receive care, is one of the fastest-growing industries globally and the international market size is expected to reach approximately USD 132 billion by 2025. In this context, it is important to examine the researches related to the subject of tourism. The purpose of this study is to analyze the articles about the subject by using the science mapping method and to reveal the motor themes. Materials and Methods: In this study; Science mapping analysis was made with the Scimat program by examining the Web of Science Core Collection (WOS) database, which is accepted from the most prestigious databases in the world. In the study, in the WOS database, the terms medical tourism and health tourism in the WOS database were scanned in the Topic tab between 1945-2019, without any index limitations, and analyzed with a total of 1357 publications. In order to evaluate the development in the field of on a periodic basis, the publications were divided into 2000-2009 and 2010-2019 periods and analyzed. Results: It has been observed that the number of publications of articles on has started to increase since 2003 and a significant increase occurred especially in 2015 (n = 181). It is seen that the most articles related to the subject are published by the United States (n = 263), followed by Canada (n = 116), England (n = 107), Malaysia (n = 85) and Australia (n = 80). When the keywords of the articles are examined, it is observed that “medical tourism” (n=647) and “health tourism” (n=164) are followed by “health care” (n=128), “tourism” (N=113) and “travel” (n=100). Three motor themes (“kidney transplantation”, “kidney” and “reproductive tourism) were included in the strategic diagram for the first period of 2000-2009, while 9 motor themes (“behavioral intention”, “medical tourism”, “intention”, “complications”, “quality”, “countries”, “Poland”, “Canada”, “Access) were included in the period 2010-2019. Conclusion: In this study, two ten – year periods between 2000 and 2019 were examined. Accordingly, while the number of articles on the subject was 108 in the first ten-year period, it increased to 1249 in the second ten-year period. It was evaluated that the reason for this was the fact that has attracted attention in the world since 2010. The ratio of Turkish articles on the subject is 0.21% (n = 3) , while the ratio of Turkish studies is 2.63% (n=36). Especially those who are interested in the subject are advised to focus on areas related to the motor themes identified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".