篇名 | 自由談的督導團體運作—精神衛生護理人員的經驗 |
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卷期 | 62:3 |
並列篇名 | The Mechanism of Free-Floating Discussion in a Psychiatric/Mental Health Nursing Supervisory Group |
作者 | 蔣欣欣 |
頁次 | 041-048 |
關鍵字 | 自由談 、 團體分析 、 深度學習 、 對話 、 督導團體 、 free-floating discussion 、 group analysis 、 deep learning 、 dialogue 、 supervisor group 、 MEDLINE 、 Scopus 、 TSCI |
出刊日期 | 201506 |
DOI | 10.6224/JN.62.3.41 |
背景:自由談或自由聯想的討論(free-floating discussion),通常應用於治療性團體,以此運作督導團體的機制並不明確。目的:本研究旨在探討自由談督導團體促成學習的運作機制。方法:採用現象學研究方法,收集進階精神衛生護理臨床實習中督導團體之內容與學習心得進行分析,督導團體是每週一次,每次150分鐘,共計12次團體。結果:自由談的督導團體具有體驗與引導兩種功能,分別是由體驗而領悟,由引導而自明。體驗的內容包括他人的經驗、身體的感知、自身的行動;引導的方式是,鼓勵陳述事例、跟隨話題的流動、引發彼此的映照。結論與應用:自由談督導團體的體驗與引導,是經由帶領者的無為而為,以及督導過程的平行運作,使臨床人員產生自發性、創造力、自信心,促進深度的學習,蘊生出實踐性智慧。
Background: Although the free-floating discussion format is widely used in group therapy, the application of this format in the context of supervisory groups has yet to be clarified. Purpose: The purpose of this study was to explore the mechanisms involved in facilitating and learning the free-floating discussion format in a supervisory group. Methods: A phenomenological approach was used to investigate the group content and personal feedback of a psychiatric- nurse supervisory group. The group held on 12 sessions. Each session was conducted once weekly and lasting 150 minutes. Results: The findings identified the functions of free-floating discussions in the context of supervisory groups as: embodied interaction and initiation by handling. Embodied interaction included: reflection on the experience of the other, sense of body, and present action. Initiation by handling included: facilitating the self-narrative, following the lead of the group, and reflecting in accordance with the group. Conclusions / Implications for Practice: The role of the facilitator is to parallel process rather than to lead in order to produce practical wisdom. Free-floating discussion and self-evidence from initiation by handling has the potential to promote spontaneity, creativity, and self-confidence in clinical practice and to promote deep learning.