COVID-19’la ilişkili psikoloji alanyazınına ilişkin bibliyometrik bir analiz

Yıl 2023, , 17 - 24, 30.04.2023

Gülşah Balaban , Ülkü Tankut Yıldırım , Muhammed Fevzi Esen

https://doi.org/10.29228/anatoljhr.68199

Cited By: 1

Öz

Amaç: Bu çalışma, COVID-19 ile ilgili psikiyatri ve psikoloji alanlarında yapılan çalışmaların bibliyometrik ve görselleştirme analizini sunmayı amaçlamaktadır.
Yöntem: 01 Ocak 2020 ile 01 Eylül 2021 tarihleri arasında gerçekleştirilen 21255 çalışma Pubmed platformu kullanılarak değerlendirilmiştir. Ayrıca, bu çalışmalar, “davranış bilimleri ve davranış mekanizmaları”, “psikolojik fenomenler”, “mental bozukluklar” ve “davranışsal disiplinler ve aktiviteler” olmak üzere dört başlık altında değerlendirilmiştir. Bildiriler, kitap bölümleri, araştırma ve derleme makalelerinden oluşan çalışmalar VOSviewer (versiyon 1.6.16) açık erişim programı ile haritalanmış ve 21255 çalışmada bulunan 6438 anahtar terimden en çok tekrarlanan 484 terim veri setine dahil edilmiştir. En çok tekrarlanan terimlerin yakınlıkları ve 6438 terim için hesaplanan 51923 bağlantıları değerlendirilerek birbirine en yakın terim kümeleri belirlenmiştir.
Bulgular: Birinci kümede anksiyete, akıl sağlığı, depresyon, psikolojik adaptasyon, psikolojik risk faktörleri, yas tutma gibi başlıkların COVID-19’la ilişkili psikoloji – psikiyatri alanında gerçekleştirilen çalışmaların büyük çoğunluğunu meydana getirdiği; ikinci kümede teletıp, enfeksiyon kontrolü, kişisel koruyucu ekipmanlar ve ayaktan tedavi hizmetleri gibi alanlarda çalışmaların yoğunlaştığı görülmektedir. Üçüncü kümedeki terimlerin büyük çoğunluğunun ise COVID-19 ve mental bozukluklar konusunda olduğu ve dördüncü kümenin sağlık iş gücünde psikolojik tükenmişlik ve iş tatmini gibi terimlerden oluştuğu gözlenmektedir.
Sonuçlar: Bu bibliyometrik çalışmada, COVID-19 ile ilgili psikiyatri ve psikoloji çalışmalarında ağırlıklı olarak hangi konuların yürütüldüğü hakkında bilgi sunulmaktadır.

Anahtar Kelimeler

psikoloji, COVID-19, bilgi yönetimi, bibliyometri

Bibliometric analysis of psychology literature related to the COVID-19 pandemic

Öz

Aim: This study aimed to provide a bibliometric and visualization analysis of the studies conducted in the fields of psychiatry and psychology related to COVID-19.
Methods: A total of 21255 studies carried on between 01 January 2020 and 01 September 2021, were obtained from Pubmed database. The studies have been evaluated under four headings: “behaviour and behaviour mechanisms”, “psychological phenomena and processes”, “mental disorders”, and “behavioral disciplines and activities”. Studies consisting of papers, book chapters, research, and review articles were mapped with the VOSviewer (version 1.6.16) open-access program, and the most repeated 484 terms out of 6438 key terms found in 21255 studies, were included in the data set. The term clusters that were closest to each other were determined by evaluating the closeness of the most repeated terms and the 51923 links calculated for 6438 terms.
Results: In the first cluster, the topics such as anxiety, mental health, depression, psychological adaptation, psychological risk factors have formed the majority of the studies in the field of psychology-psychiatry related to COVID-19; in the second cluster, studies are concentrated in areas such as telemedicine, infection control, personal protective equipment, and outpatient services. It is observed that the majority of the terms in the third cluster are about COVID-19 and mental disorders, and the fourth cluster consists of terms such as psychological burnout and job satisfaction in the health workforce.
Conclusion: In this bibliometric study, the information is provided about which topics are mainly carried out in psychiatry and psychology studies related to COVID-19.

Anahtar Kelimeler

psychology, COVID-19, knowledge management, bibliometrics

Kaynakça

• Ahmad, T., Murad, M. A., Baig, M., & Hui, J. (2021). Research trends in COVID-19 vaccine: A bibliometric analysis. Human Vaccines & Immunotherapeutics, 17(8), 2367-2372. https://doi.org/10.1080/21645515.2021.1886806
• Akintunde, T. Y., Chen, S., Musa, T. H., Amoo, F. O., Adedeji, A., Ibrahim, E., Tassang, A. E., Musa, I. H., & Musa, H. H. (2021). Tracking the progress in COVID-19 and vaccine safety research - a comprehensive bibliometric analysis of publications indexed in Scopus database. Human Vaccines & Immunotherapeutics, 17(11), 3887–3897. https://doi.org/10.1080/21645515.2021.1969851
• Al-Zaman, S. (2020). A bibliometric and co-occurence analysis of COVID-19-related literature published between December 2019 and June 2020. Science Editing, 8(1), 57-63. https://doi.org/10.6087/kcse.230
• Aristovnik, A., Ravšelj, D., & Umek, L. (2020). A Bibliometric analysis of COVID-19 across science and social science research landscape. Sustainability, 12(21), 9132. https://doi.org/10.3390/su12219132
• Bäuerle, A., Teufel, M., Musche, V., Weismüller, B., Kohler, H., Hetkamp, M., Dörrie, N., Schweda, A., & Skoda, E. M. (2020). Increased generalized anxiety, depression and distress during the COVID-19 pandemic: A cross-sectional study in Germany. Journal of Public Health, 42(4), 672-678. https://doi.org/10.1093/pubmed/fdaa106
• Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020). The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet, 395, 912-920. https://doi.org/10.1016/S0140-6736(20)30460-8
• Chen, Y., Zhang, X., Chen, S., Zhang, Y., Wang, Y., Lu, Q., & Zhao, Y. (2021). Bibliometric analysis of mental health during the COVID-19 pandemic. Asian Journal of Psychiatry, 65, 102846. https://doi.org/10.1016/j.ajp.2021.102846
• Chahrour, M., Assi, S., Bejjani, M., Nasrallah, A. A., Salhab, H., Fares, M., & Khachfe, H. H. (2020). A bibliometric analysis of COVID-19 research activity: A call for increased output. Cureus, 12(3), e7357. https://doi.org/10.7759/cureus.7357
• Choi, B., Choudhary, M. C., Regan, J., Sparks, J. A., Padera, R. F., Qiu, X., Solomon, I. H., Kuo, H. H., Boucau, J., Bowman, K., Adhikari, U. D., Winkler, M. L., Mueller, A. A., Hsu, T. Y., Desjardins, M., Baden, L. R., Chan, B. T., Walker, B. D., Lichterfeld, M., Brigl, M., … Li, J. Z. (2020). Persistence and evolution of SARS-CoV-2 in an immunocompromised host. The New England Journal of Medicine, 383(23), 2291–2293. https://doi.org/10.1056/NEJMc2031364
• Coronaviridae Study Group of the International Committee on Taxonomy of Viruses (2020). The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nature Microbiology, 5(4), 536–544. https://doi.org/10.1038/s41564-020-0695-z
• De Felice, F., & Polimeni, A. (2020). Coronavirus disease (COVID-19): A machine learning bibliometric analysis. In vivo (Athens, Greece), 34(3 Suppl), 1613–1617. https://doi.org/10.21873/invivo.11951
• Dehghanbanadaki, H., Seif, F., Vahidi, Y., Razi, F., Hashemi, E., Khoshmirsafa, M., & Aazami, H. (2020). Bibliometric analysis of global scientific research on Coronavirus (COVID-19). Medical Journal of the Islamic Republic of Iran, 34, 51. https://doi.org/10.34171/mjiri.34.51
• Dymecka, J., Gerymski, R., & Machnik-Czerwik, A. (2021). Fear of COVID-19 as a buffer in the relationship between perceived stress and life satisfaction in the Polish population at the beginning of the global pandemic. Health Psychology Report, 9(2), 149-159. https://doi.org/10.5114/hpr.2020.102136
• El-Hawary, S. S., Sobeh, M., Badr, W. K., Abdelfattah, M., Ali, Z. Y., El-Tantawy, M. E., Rabeh, M. A., & Wink, M. (2020). HPLC-PDA-MS/MS profiling of secondary metabolites from Opuntia ficus-indica cladode, peel and fruit pulp extracts and their antioxidant, neuroprotective effect in rats with aluminum chloride induced neurotoxicity. Saudi Journal of Biological Sciences, 27(10), 2829–2838. https://doi.org/10.1016/j.sjbs.2020.07.003
• Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses. FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 22(2), 338-342. https://doi.org/10.1096/fj.07-9492LSF
• Fan, J., Gao, Y., Zhao, N., Dai, R., Zhang, H., Feng, X., Shi, G., Tian, J., Chen, C., Hambly, B. D., & Bao, S. (2020). Bibliometric analysis on COVID-19: A comparison of research between English and Chinese studies. Frontiers in Public Health, 8, 477. https://doi.org/10.3389/fpubh.2020.00477
• Farooq, R. K., Rehman, S. U., Ashiq, M., Siddique, N., & Ahmad, S. (2021). Bibliometric analysis of coronavirus disease (COVID-19) literature published in Web of Science 2019-2020. Journal of Family & Community Medicine, 28(1), 1-7. https://doi.org/10.4103/jfcm.JFCM_332_20
• Gong, Y., Ma, T. C., Xu, Y. Y., Yang, R., Gao, L. J., Wu, S. H., Li, J., Yue, M. L., Liang, H. G., He, X., & Yun, T. (2020). Early research on COVID-19: A bibliometric analysis. Innovation (New York, N.Y.), 1(2), 100027. https://doi.org/10.1016/j.xinn.2020.100027
• González-Sanguino, C., Ausín, B., Castellanos, M. Á., Saiz, J., López-Gómez, A., Ugidos, C., & Muñoz, M. (2020). Mental health consequences during the initial stage of the 2020 Coronavirus pandemic (COVID-19) in Spain. Brain, Behavior and Immunity, 87, 172-176. https://doi.org/10.1016/j.bbi.2020.05.040
• Groarke, J. M., Berry, E., Graham-Wisener, L., McKenna-Plumley, P. E., McGlinchey, E., & Armour, C. (2020). Loneliness in the UK during the COVID-19 pandemic: cross-sectional results from the COVID-19 psychological wellbeing study. PLoS One, 15(9), e0239698. https://doi.org/10.1371/journal.pone.0239698
• Günay, A. (2021). Short-term impact of Covid-19 pandemic on the global and Turkish scientific publications performance. Journal of Advanced Education Studies, 3(2), 144-152. https://doi.org/10.48166/ejaes.1017116
• Hyland, P., Shevlin, M., McBride, O., Murphy, J., Karatzias, T., Bentall, R. P., Martinez, A., & Vallières, F. (2020). Anxiety and depression in the Republic of Ireland during the COVID-19 pandemic. Acta Psychiatrica Scandinavica, 142(3), 249–256. https://doi.org/10.1111/acps.13219
• Jen, G. H., Chen, S. Y., Chang, W. J., Chen, C. N., Yen, A. M., & Chang, R. E. (2021). Evaluating medical capacity for hospitalization and intensive care unit of COVID-19: A queue model approach. Journal of the Formosan Medical Association = Taiwan yi Zhi, 120 (Suppl 1), S86–S94. https://doi.org/10.1016/j.jfma.2021.05.002
• Li, L. Z., & Wang, S. (2020). Prevalence and predictors of general psychiatric disorders and loneliness during COVID-19 in the United Kingdom. Psychiatry Research, 291, 113267. https://doi.org/10.1016/j.psychres.2020.113267
• Mazza, C., Ricci, E., Biondi, S., Colasanti, M., Ferracuti, S., Napoli, C., & Roma, P. (2020). A nationwide survey of psychological distress among Italian people during the COVID-19 pandemic: Immediate psychological responses and associated factors. International Journal of Environmental Research and Public Health, 17(9), 3165. https://doi.org/10.3390/ijerph17093165
• Ornell, F., Schuch, J. B., Sordi, A. O., & Kessler, F. (2020). "Pandemic fear" and COVID-19: mental health burden and strategies. Revista Brasileira de Psiquiatria, 42(3), 232–235. https://doi.org/10.1590/1516-4446-2020-0008
• Portnoy, J., Waller, M., & Elliott, T. (2020). Telemedicine in the Era of COVID-19. The journal of allergy and clinical immunology. In Practice, 8(5), 1489–1491. https://doi.org/10.1016/j.jaip.2020.03.008
• Rumas, R., Shamblaw, A. L., Jagtap, S., & Best, M. W. (2021). Predictors and consequences of loneliness during the COVID-19 Pandemic. Psychiatry Research, 300, 113934. https://doi.org/10.1016/j.psychres.2021.113934
• Salari, N., Hosseinian-Far, A., Jalali, R., Vaisi-Raygani, A., Rasoulpoor, S., Mohammadi, M., Rasoulpoor, S., & Khaledi-Paveh, B. (2020). Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Globalization and Health, 16(1), 57. https://doi.org/10.1186/s12992-020-00589-w
• Sampaio, M., Haro, M., De Sousa, B., Melo, W. V., & Hoffman, H. G. (2021). Therapists make the switch to telepsychology to safely continue treating their patients during the COVID-19 pandemic. Virtual reality telepsychology may be next. Frontiers in Virtual Reality, 1, 576421. https://doi.org/10.3389/frvir.2020.576421
• Sohrabi, C., Alsafi, Z., O'Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., & Agha, R. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery (London, England), 76, 71–76. https://doi.org/10.1016/j.ijsu.2020.02.034
• Soytas, R. B. (2021). A bibliometric analysis of publications on COVID-19 and older adults. Annals of Geriatric Medicine and Research, 25(3), 197–203. https://doi.org/10.4235/agmr.21.0060
• Srivastava, S., & Agrawal, S. (2020). Resistance to change and turnover intention: a moderated mediation model of burnout and perceived organizational support. Journal of Organizational Change Management, 33, 1431-1447. https://doi.org/10.1108/JOCM-02-2020-0063
• Steardo, L., Steardo, L., & Verkhratsky, A. (2020). Psychiatric face of COVID-19. Translational Psychiatry, 10, 261. https://doi.org/10.1038/s41398-020-00949-5
• Van Eck, N. J., Waltman, L., Dekker, R., & Berg, J. V. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the Association for Information Science and Technology, 61(12), 2405-2416. https://doi.org/10.1002/asi.21421
• Vasantha Raju, N., & Patil, S. B. (2020). Indian Publications on SARS-CoV-2: A bibliometric study of WHO COVID-19 database. Diabetes & Metabolic Syndrome, 14(5), 1171–1178. https://doi.org/10.1016/j.dsx.2020.07.007
• Vindegaard, N., & Benros, M. E. (2020). COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain, Behavior and Immunity, 89, 531-542. https://doi.org/10.1016/j.bbi.2020.05.048
• Wang, C., & Zhao, H. (2020). The impact of COVID-19 on anxiety in Chinese university students. Frontiers in Psychology, 11, 1168. https://doi.org/10.3389/fpsyg.2020.01168
• Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in Genetics, 9, 515. https://doi.org/10.3389/fgene.2018.00515