18,747 research outputs found
Emotion-focused therapy
Emotion-focused therapy (EFT), also known as process-experiential therapy, integrates active therapeutic methods from gestalt and other humanistic therapies within the frame of a person-centred relationship (Elliott, Watson, Goldman & Greenberg, 2004). EFT updates person-centred and gestalt therapies by incorporating contemporary emotion theory and affective neuroscience, dialectical constructivism, and contemporary attachment theory. In this chapter, I review the current status of EFT, summarising its history, theory, practice, and outcome evidence
The Paradox of Emotionality & Competence in Multicultural Competency Training: A Grounded Theory
The American Psychological Association mandates multicultural competency training as a requirement of accredited doctoral programs. The tripartite model of knowledge, skills, and awareness has been the most consistently cited framework in the last two decades. Although multiple pedagogical methods have been researched, there has yet to be a unified theory developed to link educational techniques to the tripartite domain competencies. Furthermore, there is a dearth of research exploring the various learning factors involved in multicultural competency training. Emotionality is an important factor in obtaining multicultural competency. No unified theory of multicultural education can be developed without incorporating the element of emotional triggering. This grounded theory study found that the emotional construct, termed Agent Shame, served as a barrier to multicultural competency. Further, a curriculum construct coined Oppression Mechanics, offers powerful implications for future multicultural competency training
ν¬μ€μΌμ΄λ₯Ό μν λνν μΈκ³΅μ§λ₯μ λͺ¨μ¬λ νλ₯΄μλ λμμΈ λ° μ¬μ©μ κ²½ν μ°κ΅¬
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μΈλ¬Έλν νλκ³Όμ μΈμ§κ³Όνμ 곡, 2021.8. μ΄μ€ν.λμ§νΈ ν¬μ€μΌμ΄(Digital Healthcare) κΈ°μ μ λ°μ μ μΌμ ν¬μ€μΌμ΄ μμμμμ νμ μ μ£Όλ νκ³ μλ€. μ΄λ μν μ λ¬Έκ°λ€μ μ νν μ§λ¨, μ§λ³μ μΉλ£λ₯Ό λμΈ λΏλ§ μλλΌ μ¬μ©μκ° μ€μ€λ‘ μΌμμμ μκΈ°κ΄λ¦¬λ₯Ό ν μ μλλ‘ λλλ€. λμ§νΈ ν¬μ€μΌμ΄ κΈ°μ μ λνμ μΈ λͺ©ν μ€ νλλ ν¨κ³Όμ μΌλ‘ ν¬μ€μΌμ΄ μλΉμ€λ₯Ό κ°μΈν μν€λ κ²μΈλ°, μ΄λ¬ν μΈ‘λ©΄μμ λνν μΈκ³΅μ§λ₯(Conversational AI)μ μ¬μ©νκΈ° μ½κ³ ν¨μ¨μ μΈ λΉμ©μΌλ‘ κ°μΈνλ μλΉμ€λ₯Ό μ 곡ν μ μκΈ°μ μ£Όλͺ©λ°κ³ μλ€.
κΈ°μ‘΄μ κ°μΈνλ μΌμ΄ μλΉμ€λ€μ κ²½μ°λ λλΆλΆ μλ£κΈ°κ΄μ μ§λ³μΉλ£ μλΉμ€λ€μ ν¬ν¨λμλλ°, λνν μΈκ³΅μ§λ₯μ μ΄λ¬ν κ°μΈνλ μΌμ΄ μλΉμ€μ μμμ μΌμμμμ μ§λ³ μλ°©μ μν κ΄λ¦¬λ‘ νμ₯νλλ° κΈ°μ¬νλ€. μΌλμΌ λνλ₯Ό ν΅ν΄ λ§μΆ€ν κ΅μ‘, ν
λΌνΌ, κ·ΈμΈμ κ΄λ ¨ μ 보 λ±μ μ 곡ν μ μλ€λ μΈ‘λ©΄μμ μΌμ ν¬μ€μΌμ΄μ μ ν©ν λμ§νΈ ν¬μ€μΌμ΄ κΈ°μ λ‘μ νμ©λκ° λλ€. μ΄λ¬ν μ΄μ μΌλ‘ μΈν΄ λ€μν μν μ κ°μ§ λνν μΈκ³΅μ§λ₯λ€μ κ°λ°μ΄ μ΄λ£¨μ΄μ§κ³ μλ€.
κ·Έλ¬λ, μ΄λ¬ν λνν μΈκ³΅μ§λ₯λ€μκ² μ¬μ©μμ μ νΈλμ μ ν©ν νλ₯΄μλλ₯Ό λΆμ¬νλ μ°κ΅¬λ λλ¬Όκ² μ΄λ£¨μ΄ μ§κ³ μλ€. λνν μΈκ³΅μ§λ₯μ μ£Όμ κΈ°λ₯μΈ μμ°μ΄ κΈ°λ° μνΈμμ©μ CASA ν¨λ¬λ€μ(CASA Paradigm)μμ μ κΈ°νλ μ¬μ©μκ° μμ€ν
μ μμΈννλ κ²½ν₯μ λμΈλ€. λλ¬Έμ νλ₯΄μλμ λν μ¬μ©μμ μ νΈλκ° μ§μμ μΈ λνν μΈκ³΅μ§λ₯μ μ¬μ©κ³Ό λͺ°μ
μ μν₯μ λ―ΈμΉλ€. λν λνν μΈκ³΅μ§λ₯μ μ₯κΈ°μ μΈ μ¬μ©μ μν΄μ μ μ ν μ¬μ©μμμ μ¬νμ , κ°μ μ μνΈμμ©μ λμμΈ ν΄ μ£Όμ΄μΌ νλλ°, μΈμ§λ νλ₯΄μλμ λν μ¬μ©μμ μ νΈλκ° μ΄ κ³Όμ μλ μ μλ―Έν μν₯μ λ―ΈμΉλ€. λλ¬Έμ μ§μμ μΈ μ°Έμ¬κ° κ²°κ³Όμ ν° μν₯μ λ―ΈμΉλ μΌμ ν¬μ€μΌμ΄ μμμμ λνν μΈκ³΅μ§λ₯μ νμ©νλλ° κ°μΈνλ νλ₯΄μλ λμμΈμ΄ κΈμ μ μΈ μ¬μ©μ κ²½ν λ° μ¬μ©μ κ±΄κ° μ¦μ§μ κ°λ₯μ±μ λμΌ κ²μΌλ‘ λ³Έ μ°κ΅¬λ κ°μ νλ€. κ°μΈνλ νλ₯΄μλ λμμΈμ μν΄ μ¬μ©μμ νμ€μμ μΉλ°ν κ΄κ³μ μλ μ€μ‘΄μΈλ¬Ό(νΈμ€νΈ)μ νλ₯΄μλλ₯Ό λνν μΈκ³΅μ§λ₯μ μ μ©νκ³ νκ°νλ κ²μ΄ λ³Έ μ°κ΅¬μ ν΅μ¬μ μΈ μμλμ΄μ΄λ€.
μ΄λ₯Ό κ²μ¦νκΈ° μν΄μ ν΄λΉ νμ λ
Όλ¬Έμ μ΄ μΈ κ°μ§μ μΈλΆ μ°κ΅¬λ₯Ό ν¬ν¨νλ€. 첫째λ μ€μ‘΄μΈλ¬Όμ νλ₯΄μλ μ€μμλ μΌμ 건κ°κ΄λ¦¬μ μ ν©ν νΈμ€νΈμ νλ₯΄μλλ₯Ό νμνλ μ°κ΅¬μ΄λ€. λμ§Έλ νΈμ€νΈμ νλ₯΄μλλ₯Ό λνν μΈκ³΅μ§λ₯μ μ μ©νκΈ° μν΄ κ³ λ €ν΄μΌ ν μΈμ΄μ μμλ€μ μ μνλ μ°κ΅¬μ΄λ€. λ§μ§λ§μΌλ‘λ μμ κ³Όμ μ ν΅ν΄ κ°λ°λ μ€μ‘΄νλ μΈλ¬Όμ νλ₯΄μλλ₯Ό κ°μ§ λνν μΈκ³΅μ§λ₯μ΄ μΌμ ν¬μ€μΌμ΄ μμμμ μ€μ ν¨κ³Όλ₯Ό 보μ΄λμ§λ₯Ό νκ°νλ μ°κ΅¬μ΄λ€. λν ν΄λΉ νμλ
Όλ¬Έμ μΌλ ¨μ μ°κ΅¬λ€μμ λ°κ²¬ν κ²°κ³Όλ€μ λ°νμΌλ‘ μ¬μ©μμ μΉλ°ν κ΄κ³μ μλ νλ₯΄μλλ₯Ό μΌμ ν¬μ€μΌμ΄λ₯Ό μν λνν μΈκ³΅μ§λ₯μ μ μ©ν λ κ³ λ €ν΄μΌν λμμΈ ν¨μμ λ€μ λμΆνκ³ κ°μ΄λλΌμΈμ μ μνλ€.Advance in digital healthcare technologies has been leading a revolution in healthcare. It has been showing the enormous potential to improve medical professionalsβ ability for accurate diagnosis, disease treatment, and the usersβ daily self-care. Since the recent transformation of digital healthcare aims to provide effective personalized health services, Conversational AI (CA) is being highlighted as an easy-to-use and cost-effective means to deliver personalized services.
Particularly, CA is gaining attention as a mean for personalized care by ingraining positive self-care behavior in a daily manner while previous methods for personalized care are focusing on the medical context. CA expands the boundary of personalized care by enabling one-to-one tailored conversation to deliver health education and healthcare therapies. Due to CA's opportunities as a method for personalized care, it has been implemented with various types of roles including CA for diagnosis, CA for prevention, and CA for therapy.
However, there lacks study on the personalization of healthcare CA to meet user's preferences on the CA's persona. Even though the CASA paradigm has been applied to previous studies designing and evaluating the human-likeness of CA, few healthcare CAs personalize its human-like persona except some CAs for mental health therapy. Moreover, there exists the need to improve user experience by increasing social and emotional interaction between the user and the CA. Therefore, designing an acceptable and personalized persona of CA should be also considered to make users to be engaged in the healthcare task with the CA. In this manner, the thesis suggests an idea of applying the persona of the person who is in a close relationship with the user to the conversational CA for daily healthcare as a strategy for persona personalization. The main hypothesis is the idea of applying a close person's persona would improve user engagement. To investigate the hypothesis, the thesis explores if dynamics derived from the social relationship in the real world can be implemented to the relationship between the user and the CA with the persona of a close person.
To explore opportunities and challenges of the research idea, series of studies were conducted to (1) explore appropriate host whose persona would be implemented to healthcare CA, (2) define linguistic characteristics to consider when applying the persona of a close person to the CA, and (3)implement CA with the persona of a close person to major lifestyle domains. Based on findings, the thesis provides design guidelines for healthcare CA with the persona of the real person who is in a close relationship with the user.Abstract 1
1 Introduction 12
2 Literature Review 18
2.1 Roles of CA in Healthcare 18
2.2 Personalization in Healthcare CA 23
2.3 Persona Design CA 25
2.4 Methods for Designing Chatbotβs Dialogue Style 30
2.4.1 Wizard of Oz Method 32
2.4.2 Analyzing Dialogue Data with NLP 33
2.4.3 Participatory Design 35
2.4.4 Crowdsourcing 37
3 Goal of the Study 39
4 Study 1. Exploring Candidate Persona for CA 43
4.1 Related Work 44
4.1.1 Need for Support in Daily Healthcare 44
4.1.2 Applying Persona to Text-based CA 45
4.2 Research Questions 47
4.3 Method 48
4.3.1 Wizard of Oz Study 49
4.3.2 Survey Measurement 52
4.3.3 Post Interview 54
4.3.4 Analysis 54
4.4 Results 55
4.4.1 System Acceptance 56
4.4.2 Perceived Trustfulness and Perceived Intimacy 57
4.4.3 Predictive Power of Corresponding Variables 58
4.4.4 Linguistic Factors Affecting User Perception 58
4.5 Implications 60
5 Study 2. Linguistic Characteristics to Consider When Applying Close Personβs Persona to a Text-based Agent 63
5.1 Related Work 64
5.1.1 Linguistic Characteristics and Persona Perception 64
5.1.2 Language Component 66
5.2 Research Questions 68
5.3 Method 69
5.3.1 Modified Wizard of Oz Study 69
5.3.2 Survey 72
5.4 Results 73
5.4.1 Linguistic Characteristics 73
5.4.2 Priority of Linguistic Characteristics 80
5.4.3 Differences between language Component 82
5.5 Implications 82
6 Study3.Implementation on Lifestyle Domains 85
6.1 Related Work 86
6.1.1 Family as Effective Healthcare Provider 86
6.1.2 Chatbots Promoting Healthy Lifestyle 87
6.2 Research questions 94
6.3 Implementing Persona of Family Member 95
6.3.1 Domains of Implementation 96
6.3.2 Measurements Used in the Study 97
6.4 Experiment 1: Food Journaling Chatbot 100
6.4.1 Method 100
6.4.2 Results 111
6.5 Experiment 2: Physical Activity Intervention 128
6.5.1 Method 131
6.5.2 Results 140
6.6 Experiment 3: Chatbot for Coping Stress 149
6.6.1 Method 151
6.6.2 Results 158
6.7 Implications from Domain Experiments 169
6.7.1 Comparing User Experience 170
6.7.2 Comparing User Perception 174
6.7.3 Implications from Study 3 183
7 Discussion 192
7.1 Design Guidelines 193
7.2 Ethical Considerations 200
7.3 Limitations 206
8 Conclusion 210
References 212
Appendix 252
κ΅λ¬Έμ΄λ‘ 262λ°
Research for Context-respectful counseling agent
ζ±δΊ¬ι»ζ©ε€§ε¦201
- β¦