WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6

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Author: search.filters.author.Dikec, GulPublisher: search.filters.publisher.Taylor & Francis inc

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Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Protocol Registration and Reporting of Systematic Review and Meta-Analyses Published in Psychiatric and Mental Health Nursing Journals: a Descriptive Study
    (Taylor & Francis inc, 2023) Dikec, Gul; Ozer, Duygu
    Although it is not mandatory for systematic review and meta-analysis studies, protocol registration is essential in the prevention of biases. This study aims to investigate the protocol registration status and reporting of systematic reviews and meta-analyses published in psychiatric nursing journals. This descriptive study's data were obtained by scanning the 10 mental health and psychiatric nursing journals in which the studies of psychiatric nurses were most frequently published and by examining the systematic reviews and meta-analyses published between 2012-2022. A total of 177 completed studies have been reviewed. It was determined that 18.6% of the examined systematic reviews and meta-analyses had a protocol registration. Almost all (96.9%) of the registered studies were registered with PROSPERO, and 72.7% were registered prospectively. It was determined that the registration status of the studies changed statistically according to the country where the studies' authors were located. When the published studies were examined, it was determined that approximately one out of every five studies were registered. With the prospective registration of systematic reviews, biases could be prevented, and evidence-based interventions can be made based on the knowledge obtained.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Qualitative and Artificial Intelligence-Based Sentiment Analyses of Anti-Lgbti Plus Hate Speech on Twitter in Turkey
    (Taylor & Francis inc, 2023) Dogan, M. Berna; Oban, Volkan; Dikec, Gul
    The aim of this study was to evaluate hate speech in Turkish LGBTI+-related tweets during a one-month period of artificial intelligence-based sentiment analyses. Turkish tweets related to LGBTI+, were retrieved using Python library Tweepy and were evaluated by sentiment analysis. The researchers then performed a qualitative analysis of the most frequently liked and retweeted tweets (n = 556). Sentiment analysis revealed that 69.5% of tweets were negative, 23.3% were neutral, and 7.2% were positive. The qualitative analysis was grouped under seven themes: LGBTI+ Club; Terrorism and Terrorist Organization Membership; Perversion, Illness, Immorality; Presence in History; Religious References; Insults; and Humiliation. The results of this study show that anti-LGBTI+ hate speech in Turkey is significant in terms of both quality and quantity. As LGBTI+ individuals are at risk for excess mental distress and disorders, it is important to understand the risks and other factors that ameliorate stress and contribute to mental health in social media.