Sağlık turizmi konulu yayınların bilim haritalama yöntemiyle analizi
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».