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search.filters.applied.f.publicationcategory: Makale - Uluslararası Hakemli Dergi - Öğrenci × Konu: Controlled study ×

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    Examining psychological resilience and posttraumatic growth following terrorist attacks in Turkey
    (American Psychological Association, 2021-06) İkizer, Gözde; Özel, Ebru Pelin
    Acts of terrorism, being highly prevalent across the world, disrupt community and social functioning and can lead to negative psychological reactions in individuals. However, positive outcomes can also be evoked after adverse experiences. The current study aimed to explore two salutogenic or positive outcomes—resilience and posttraumatic growth (PTG)—following exposure to terrorist attacks. The sample included 331 university students who were exposed to a terrorist attack in Turkey during the last 18 months prior to data collection. Participants responded to the Connor-Davidson Resilience Scale, the Posttraumatic Growth Inventory, and a participant information form. The relationship between resilience and PTG was examined through correlation analysis and regression analyses with linear and quadratic components. Resilience and PTG were positively correlated. Tendency toward spirituality was the only resilience domain that was significantly correlated with all domains of growth. Total score of resilience was significantly associated with scores on all subscales of the Posttraumatic Growth Inventory except appreciation of life. Results indicated that only linear relationships existed between domains of resilience and PTG in the study sample. The positive and linear association between resilience and PTG suggests that resilience may be an important tool for facilitating growth. After terrorist attacks, mental health care planning should adopt a patient-centered approach that acknowledges the possibility of positive outcomes following traumatic events and focuses on the impact as well as recovery phases in traumatized individuals.
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    Theta and Beta1 frequency band values predict dyslexia classification
    (John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja Abou
    Dyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.