Self-compassion and automatic thoughts

Bu araştırmanın amacı öz-duyarlık ile otomatik düşünceler arasındaki ilişkileri incelemektir. Araştırmanın örneklemini 299 üniversite öğrencisi oluşturmaktadır. Katılımcılara Öz-duyarlık Ölçeği ve Otomatik Düşünceler Ölçeği uygulanmıştır. Öz-duyarlık ile otomatik düşünceler arasındaki ilişkileri incelemek için yapılan korelasyon sonucunda öz-duyarlığm; öz-sevecenlik, paylaşımların bilincinde olma ve bilinçlilik alt boyutlarının otomatik düşünceler ile negatif, öz-yargılama, izolasyon ve aşırı özdeşleşme boyutlarının ise pozitif ilişkili olduğu görülmüştür. Otomatik düşüncelerin öz-duyarlık tarafından açıklanma düzeyini belirlemek amacıyla kurulan yapısal eşitlik modelinden elde edilen uyum indeksleri modelin iyi uyum verdiğini göstermiştir$(X^{2}$ =0.05, p =.03832, GFI =1.00, AGFI =.96, CFI =1.00, NFI =1.00, IFI =1.00, RFI =.97, SRMR =.014 ve RMSEA =.038). Öz-duyarlığın otomatik düşünceleri açıklama oranı $R^{2}=.39$ olarak bulunmuştur. Path analizi sonuçları, öz-duyarlığm öz-sevecenlik, paylaşımların bilincinde olma ve bilinçlilik boyutlarının otomatik düşünceleri negatif yönde, öz-yargılama ve aşırı özdeşleşme boyutlarının ise otomatik düşünceleri pozitif yönde yordadığını ortaya koymuştur. Bulgular literatür ışığında tartışılmıştır.

Öz-duyarlılık ve otomatik düşünceler

The aim of this research is to examine the relationships between self-compassion and automatic thoughts. Participants were 299 university students. In this study, the Self-compassion Scale and the Automatic Thoughts Questionnaire were used. The relationships between self-compassion and automatic thoughts were examined using correlation analysis and the hypothesis model was tested through structural equation modeling. In correlation analysis, self-kindness, common humanity, and mindfulness factors of self-compassion were found negatively related to automatic thoughts. On the other hand, self-judgment, isolation, and over-identification factors of self-compassion were found positively correlated to automatic thoughts. The model demonstrated excellent fit $(X^{2}$ =0.05, p =.03832, GFI =1.00, AGFI = 96, CFI =1.00, NFI =1.00, IFI =1.00, RFI = 97, SRMR = 014, and RMSEA = 038) and also accounted for 39% of the automatic thoughts variances. According to path analysis results, automatic thoughts were predicted negatively by self-kindness, common humanity, and mindfulness. On the other hand, self-judgment and over-identification predicted automatic thoughts in a positive way. However, the path from isolation to automatic thoughts wasn't significant. Results were discussed in the light of the related literature.

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