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모래놀이치료의 치료자-내담자 관계 모형에 근거한역전이 척도 개발 연구
안은선,장미경,니콜라스 신 사단법인 한국임상모래놀이치료학회 2023 상징과 모래놀이치료 Vol.14 No.3
In this study, we aimed to develop a countertransference scale based on the therapist-client relationship model in Sandplay Therapy. Additionally, we sought to validate the developed scale by examining its relationship with therapeutic alliance and the therapeutic relationship. Sandplay Therapy practitioners participated in data collection. The collected data were analyzed by calculating frequencies and percentages, conducting factor analysis, reliability testing, and correlation analysis. As a result, the countetransference scale comprised 30 items organized into five subfactors: ‘Mother-Child,’ ‘Power,’ ‘Scenery,’ ‘Shamanism,’ and ‘Wounded Healer.’ The validation of construct validity revealed significant positive correlations between the entire countertransference scale and the entire therapeutic alliance scale, as well as between the entire countertransference scale and the entire therapeutic relationship scale. The analysis revealed significant positive correlations between the overall reverse transfer scale and the alliance scale, as well as significant positive correlations between the overall reverse transfer scale and the therapeutic relationship scale.
최원식,프라타마 판두 산디,수페노 데스티아니,변재영,이은숙,우지희,양지웅,키프 디마스 하리스 신,크리스타 마이난다 브리기타,오케추쿠 나에메카 니콜라스,이강삼,Choi, Wonsik,Pratama, Pandu Sandi,Supeno, Destiani,Byun, Jaeyoung,Lee, Ensuk,Woo, Jihee,Yang, Jiung,Keefe, Dimas The Korean Society of Industry Convergence 2018 한국산업융합학회 논문집 Vol.21 No.5
In this research, the effect of normal load, sliding velocity, and texture density on thefriction coefficient of surfaces micro-textured on AISI 4140 under paraffin oil lubrication were investigated. The predicted tribological behavior by numerical calculation can be serves as guidance for the designer during the machine development stage. Therefore, in this research friction coefficient prediction model based on response surface methodology (RSM), support vector machine (SVM), and artificial neural network (ANN) were developed. The experimental result shows that the variation of load, speed and texture density were influence the friction coefficient. The RSM, ANN and SVM model was successfully developed based on the experimental data. The ANN model can effectively predict the tribological characteristics of micro-textured AISI 4140 in paraffin oil lubrication condition compare to RSM and SVM.