1980-2024 yılları arasında birincil sağlık hizmetleri ile diyabet arasındaki ilişkiler üzerine küresel araştırma eğilimleri: makine öğrenmesi tabanlı bilimsel haritalama çalışması

Öz

Amaç: Diyabetle ilgili yapılan çalışmaların sistematik ve ayrıntılı olarak incelenmesi, sağlık hizmetlerinin değerlendirilmesine, sorunların belirlenmesine ve önleyici müdahalelere rehberlik edebilir. Bu nedenle çalışmamız kullanılan makine öğrenmesi destekli bibliyometrik yöntem ile birincil sağlık hizmetleri (BSH) araştırma alanındaki diyabet araştırmalarını değerlendirmeyi amaçlamıştır. Gereç ve yöntem: Çalışmamızda 15 Mart 2024 tarihinde Web of Science Core Collection veri kaynağından elde edilen diabet konulu birinci basamak sağlık hizmetleri araştırma alanındaki makaleler bibliyometrik yöntemler ve Latent Dirichlet Allocation (LDA) konu modelleme yöntemi ile analiz etmektedir. Bulgular: Çalışmalar incelendiğinde BSH alanındaki diyabet konusunda 3,355 makalenin 114 farklı ülkeden, 4,226 farklı kurum ve 12,426 farklı araştırmacı tarafından üretildiği görülmüştür. Son yıllarda birinci basamak sağlık hizmetleri alanında obezite, hipertansiyon, kronik hastalık, egzersiz, fiziksel aktivite konu başlıklarının yoğun ilgi gördüğü görülmüştür. Yapılan konu modellemesi ile sekiz farklı gizli konu kümesi bulunmuştur. Bunlar sırasıyla; (1) BSH’de diyabet yönetimi, (2) BSH’de diyabetin risk faktörleri ve yönetimi (3) BSH’de diyabetin akut ve kronik komplikasyonları (4) gestasyonel diyabet (5) prediyabet, tip 1 diyabet (6) COVID-19 ve diyabet (7) yaşam kalitesi, farkındalık ve sağlık eğitimi, (8) güncel tedavi yöntemleri ve rehber kaynaklar şeklindedir. Sonuç: Primary Care Diabetes ve Journal of Family Medicine and Primary Care dergileri BSH alanında diyabet araştırmalarında öne çıkan dergilerdir. Diyabet araştırmaları ile hipertansiyon ve obezite konularının yoğun şekilde birlikte işlendiği görülmüştür. Gelecek çalışmalar için BSH alanında diyabetik retinopati ve diyabetik yara çalışmalarının yapılması önerilmektedir.

Anahtar Kelimeler

• Birinci basamak sağlık hizmetleri
• diyabet
• veri analizi
• bilim haritalama
• konu modelleme

Global research trends on the links between primary health care and diabetes from 1980 to 2024: a machine learning-based science mapping

Abstract

Purpose: A systematic and detailed examination of studies on diabetes can guide the evaluation of healthcare services, the identification of issues, and the implementation of preventive interventions. Therefore, this study aims to assess diabetes research in the primary health care (PHC) field using machine learning-based bibliometric methods. Materials and methods: In this study, articles related to diabetes in the PHC field were obtained from the Web of Science Core Collection on March 15, 2024. These articles were analyzed using bibliometric methods and the Latent Dirichlet Allocation (LDA) topic modeling technique. Results: The analysis of the studies revealed that 3.355 articles on diabetes in the PHC field were produced by researchers from 114 different countries, 4.226 institutions, and 12.426 individual researchers. Recent years have shown a growing interest in topics such as obesity, hypertension, chronic diseases, exercise, and physical activity within the PHC field. Topic modeling identified eight distinct latent topic clusters: (1) Diabetes management in PHC, (2) Risk factors and management of diabetes in PHC, (3) Acute and chronic complications of diabetes in PHC, (4) Gestational diabetes, (5) Prediabetes and Type 1 diabetes, (6) COVID-19 and diabetes, (7) Quality of life, awareness, and health education, (8) Current treatment methods and guideline resources. Conclusion: Primary Care Diabetes and Journal of Family Medicine and Primary Care are the leading journals in PHC-related diabetes research. The studies show a significant overlap between diabetes research and topics such as hypertension and obesity. Future studies in the PHC field are recommended to focus on diabetic retinopathy and diabetic wound research.

Keywords

• Primary health care
• diabetes
• data analysis
• science mapping
• topic modeling

Kaynakça

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