INVESTIGATION OF THE STUDIES PUBLISHEDON WOS BETWEEN 2013-2017 IN THE FIELD OF HEALTH TOURISM WORLDWIDE
Bibliographic record
Abstract
The aim of this study, is to examine studies searched on WOS in the field of health tourism worldwide in the last five years in terms of different variables. The study's universe is made up of the WOS databases from 2013 to 2017 and includes studies with the words “health tourism”,” medical tourism”,” thermal tourism”, “disabled tourism”, “ elderly tourism” and “spa-wellness”. Only "research articles" were examined. In total 212 articles were identified, of which 160 were reached and examined according to criteria determined by the researchers. In the studies, which is reached as full text, "Medical Tourism" was the most commonly used keyword. It was determined that the studies were mostly published in 2015 (45) and English (149) was the commonly preferred language of the studies. It has been found that the studies conducted are mostly written by two authors (43) and the authors of all 160 studies mostly work in departments related to Health, Business Administration and Geography. Studies in the field of health tourism were made mostly in Malaysia (18), Korea (16) and Canada (13), respectively. In the studies examined, focus group interview / deep interview / interview technique (40) was used as a research method. *This paper is an extended version of the study which was presented in 8th International Health Tourism Congress in Aydın, Turkey
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".