-
01
Subject 1: compassion
1
-
02
Strauss, C., Lever Taylor, B., Gu, J., Kuyken, W., Baer, R., Jones, F., & Cavanagh, K. (2016). What is compassion and how can we measure it? A review of definitions and measures. Clinical Psychology Review, 47, 15–27. https://doi.org/10.1016/J.CPR.2016.05.004
1
-
1. Why This Paper Is Important
1
-
2. Aim and Structure of the Paper
1
-
3. The Challenge: No Consensus Definition
1
-
4. From Three Facets to Five Elements
2
-
5. The Five-Element Definition of Compassion
2
-
6. Compassion Versus Empathy
2
-
7. Are Self-Compassion and Compassion for Others the Same Construct?
3
-
8. The Systematic Review of Measures
3
-
9. Why Measurement Matters
4
-
10. Final Conclusion
4
-
03
van Lotringen, C. M., Jeken, L., Westerhof, G. J., ten Klooster, P. M., Kelders, S. M., & Noordzij, M. L. (2021). Responsible Relations: A Systematic Scoping Review of the Therapeutic Alliance in Text-Based Digital Psychotherapy. Frontiers in Digital Health, 3(July). https://doi.org/10.3389/fdgth.2021.689750
5
-
1.Technology and the Therapeutic Alliance
5
-
2. What is the Therapeutic Alliance?
5
-
3. What is Text-Based Digital Psychotherapy?
5
-
4. The Promise of Text-Based Therapy
6
-
5. What Did the Review Find?
6
-
6. How Strong Was the Therapeutic Alliance?
7
-
7. Alliance and Outcome
7
-
8. Responsible Application of Text-Based Digital Psychotherapy
7
-
9. But: Important Limitations
8
-
10. Ethical and Cultural Limitations
8
-
04
Kemp, J., Zhang, T., Inglis, F., Wiljer, D., Sockalingam, S., Crawford, A., ... & Strudwick, G. (2020). Delivery of compassionate mental health care in a digital technology–driven age: scoping review. Journal of Medical Internet Research, 22(3), e16263. doi: 10.2196/16263
9
-
1. Why This Review Was Necessary
9
-
2. Working Definition of Compassion
9
-
3. Research Question 1: What Digital Technologies Are Used?
9
-
4. Research Question 2: How Do Digital Technologies Intersect With Compassion?
10
-
5. Research Question 3: Facilitators and Barriers
12
-
6. Comparison with Feijt et al. (2018)
13
-
7. Overall Conclusion
14
-
05
van Lotringen C, Lusi B, Westerhof G, Ludden G, Kip H, Kelders S, Noordzij M. The Role of Compassionate Technology in Blended and Digital Mental Health Interventions: Systematic Scoping Review. JMIR Ment Health 2023;10:e42403. URL: https://mental.jmir.org/2023/1/e42403. DOI: 10.2196/42403
14
-
Why was this review needed?
14
-
What is Compassion? (STRAUSS et al., 2016)
15
-
The Three Compassionate Roles of Technology
15
-
How Do DMHIs Support Each Element of Compassion?
17
-
Compassionate Technology in Blended Treatment
18
-
Critical Notes: Opportunities vs Risks
19
-
Key Theoretical Insight
19
-
Big Picture Conclusion
19
-
06
Morrow, E., Zidaru, T., Ross, F., Mason, C., Patel, K. D., Ream, M., & Stockley, R. (2023). Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Frontiers in Psychology, 13, 971044. https://doi.org/10.3389/fpsyg.2022.971044
20
-
1. Why Is AI Often Seen as Incompatible with Compassion?
20
-
2. Compassion as a Motivational System
21
-
3. Reconceptualizing Compassion as a Human–AI System of Intelligent Caring
21
-
4. Why See Compassion as a System?
22
-
5. How AI Technologies Enhance Compassion (From the Review Findings)
22
-
6. The Ethical Debate: Replication vs Transformation
23
-
7. Implications of the Human–AI System of Intelligent Caring
24
-
8. Limitations Acknowledged by the Authors
24
-
07
van Lotringen, C. M., Zirnheld, A., Kelders, S. M., Westerhof, G. J., & Noordzij, M. L. (2025). Compassion as a guiding framework for the implementation of digital mental health interventions: An interview study with clients and professionals. PLoS One, 20(10), e0320710. https://doi.org/10.1371/journal.pone.0320710
25
-
1. Compassion as a Guiding Framework for the Implementation of Digital Mental Health Interventions
25
-
2.Clients: What Do They Value?
26
-
3.Professionals: What Motivates Them?
26
-
4.Technology Through the Lens of Compassion
27
-
5.Matches and Mismatches With Organizational Motivation
27
-
6.Compassion as an Implementation Framework
27
-
08
Subject 2; Attitude of (mental) health professionals & implementation in clinical practice
28
-
09
Feijt, M. A., de Kort, Y. A., Bongers, I. M. & IJsselsteijn, W. A. Perceived Drivers and Barriers to the Adoption of eMental Health by Psychologists: The Construction of the Levels of Adoption of eMental Health Model. J. Med. Internet Res. 20, e153 (2018). https://www.jmir.org/2018/4/e153/
28
-
The Four Main Themes Identified
28
-
1. General characteristics of eMental health
28
-
2. Drivers for Adoption
29
-
Accelerating the treatment process
29
-
Increasing client engagement between sessions
29
-
Intensifying therapy through more frequent contact
29
-
Enhancing the therapeutic alliance
29
-
3. Barriers for Adoption
29
-
Professional identity
29
-
Ethical concerns
29
-
Fear of increased responsibility
29
-
Organizational pressures
30
-
4. Contextual Factors in Daily Clinical Practice
30
-
The Theoretical Link: Rogers’ Diffusion of Innovations Theory (DIT)
30
-
1. Innovators
30
-
2. Early adopters
30
-
3. Early majority
30
-
4. Late majority
30
-
5. Laggards
30
-
1. No use – Skepticism and resistance; barriers dominate.
30
-
2. Minimal use – Some belief in benefits, but no integration into routine.
31
-
3. Passive use – eMental health becomes part of daily routines, but limited exploration.
31
-
4. Active use – High intrinsic motivation; active search for new tools.
31
-
5. Innovative use – Entrepreneurial behavior; initiating projects and envisioning systemic change.
31
-
How Was the Model Constructed?
31
-
1. Thematic analysis of interview data
31
-
2. Identification of patterns in differences between participants
31
-
3. Mapping these patterns onto Rogers’ DIT
31
-
4. Developing five empirically grounded adoption levels
31
-
5. Communicative validation with participants
31
-
Empirical grounding
31
-
Theoretical anchoring
31
-
Participant validation
31
-
How Can the LAMH Model Be Used for Implementation?
31
-
Level 1 → Increase awareness of benefits, reduce resistance
31
-
Level 2 → Provide training and practical integration support
31
-
Level 3 → Offer guiding principles and broaden knowledge
31
-
Level 4 → Provide peer support and innovation opportunities
31
-
Level 5 → Facilitate entrepreneurship and development
31
-
Understanding the “Reluctance” Question
31
-
Concern for therapeutic quality
31
-
Ethical responsibility
31
-
Fear of increased workload
31
-
Lack of time and support
32
-
Need for demonstrated benefits
32
-
Professional identity
32
-
Organizational constraints
32
-
Risk perception
32
-
Insufficient positive experience
32
-
10
Wiljer, D., Charow, R., Zhang, M., Lo, B., Sequeira, L., Shen, N., ... & Strudwick, G. (2025). Enabling Digital Compassion in Digital Health Environments: Modified eDelphi Study to Identify Interprofessional Competencies and Technology Attributes. Journal of Medical Internet Research, 27, e66547. https://doi.org/10.2196/66547
32
-
Aim of the Study
32
-
Identify essential digital compassion competencies for HCPs
32
-
Identify digital compassion attributes for digital health technologies
32
-
4. The Digital Compassion Framework (Table 4)
33
-
58 professional competencies
33
-
15 technology attributes
33
-
The 7 Domains
33
-
1. Digital literacy
33
-
→ As an HCP, you consciously explain digital tools to patients and reflect on how the medium shapes communication during each encounter.
33
-
→ As a policy advisor, you embed relational digital skills into training programs and competency frameworks, not just technical instruction.
33
-
2. Ethical implications
33
-
→ As an HCP, you assess digital access, literacy, and privacy constraints before recommending digital care.
33
-
→ As a policy advisor, you implement equity audits and ensure inclusive, accessible digital infrastructure.
33
-
3. Collaboration and co-design
33
-
→ As an HCP, you involve patients in decisions about whether and how digital tools are used in their treatment.
33
-
→ As a policy advisor, you include patient representatives in procurement and system design processes.
33
-
4. Patient preferences
33
-
→ As an HCP, you actively ask patients about their preferred communication mode and adapt accordingly.
34
-
→ As a policy advisor, you avoid “digital by default” policies and ensure flexibility across service delivery models.
34
-
5. Therapeutic relationship
34
-
→ As an HCP, you maintain eye contact through the camera, avoid multitasking, and verbally acknowledge emotions more explicitly in virtual sessions.
34
-
→ As a policy advisor, you provide training and guidelines on digital professionalism and virtual relational skills.
34
-
6. Patient safety
34
-
→ As an HCP, you explicitly assess emotional safety during virtual encounters and clarify crisis procedures.
34
-
→ As a policy advisor, you ensure that digital platforms include clear crisis-response protocols and secure escalation systems.
34
-
7. Technology safety
34
-
→ As an HCP, you use only secure platforms and clearly communicate privacy safeguards to patients.
34
-
→ As a policy advisor, you invest in interoperable, secure systems and mandate strict privacy standards.
34
-
Technology Attributes
34
-
6. Table 6 – Linking to the Conceptual Model
35
-
1. Awareness of suffering
35
-
2. Mediated response
35
-
3. Online intervention
35
-
4. Training and coaching
35
-
5. Compassion-oriented technologies
35
-
6. Personalized response (AI potential)
35
-
Key Discussion Insights
35
-
Existing digital health competency frameworks focus on technical skills.
35
-
They rarely address the digital therapeutic relationship.
35
-
Compassion must be embedded across:
36
-
Education
36
-
Policy
36
-
Design
36
-
Organizational culture
36
-
A cultural shift
36
-
Compassion-centered co-design
36
-
Ongoing evaluation
36
-
Integration into digital literacy training
36
-
How to Think About Operationalization
36
-
8. If You Were a Mental Health Professional…
36
-
Explicitly acknowledging emotions during video sessions
36
-
Clarifying communication expectations
36
-
Checking digital access barriers
36
-
Ensuring safety in virtual crisis situations
36
-
Reflecting on how technology changes power dynamics
36
-
9. If You Were a Policy Advisor…
36
-
Embedding digital compassion competencies into training standards
36
-
Including patient partners in procurement
36
-
Creating evaluation frameworks for compassion in digital tools
36
-
Mandating equity assessments
36
-
Investing in secure and interoperable systems
36
-
11
Reynolds, J., Griffiths, K. M., Cunningham, J. A., Bennett, K., & Bennett, A. (2015). Clinical practice models for the use of e-mental health resources in primary health care by health professionals and peer workers: a conceptual framework. JMIR Mental Health, 2(1), e4200. doi: 10.2196/mental.4200
37
-
1. Why Was This Paper Written?
37
-
A clear conceptual framework
37
-
Clearly differentiated practice models
37
-
Guidance for training and policy
37
-
2. The Core Idea: Five Practice Models
37
-
The role of the service provider
37
-
The role of the e-mental health resource
37
-
The intensity of provider involvement
37
-
The required skills
37
-
Referral models (Models 1–3)
37
-
Integrative models (Models 4–5)
37
-
3. The Five Models Explained
38
-
Competence in clinical assessment
38
-
Knowledge of specific e-mental health programs
38
-
Ability to evaluate appropriateness and risk
38
-
Encouragement and motivation
39
-
Monitoring progress
39
-
Reinforcing task completion
39
-
Providing clarification
39
-
Offering emotional support
39
-
Psychoeducation
39
-
Self-monitoring
39
-
Skills practice
39
-
Homework exercises
39
-
To address comorbid conditions
40
-
As a supplement to intensive therapy
40
-
To reinforce skills between sessions
40
-
As relapse prevention
40
-
Why Are These Models Necessary?
40
-
Training programs lack focus.
40
-
Implementation strategies remain vague.
40
-
Research findings are difficult to compare.
40
-
What Is Still Missing?
40
-
Which model is most effective in routine primary care.
40
-
Which model is safest.
41
-
How much professional time is optimal.
41
-
What training best supports uptake.
41
-
The Core Continuum You Must Understand
41
-
Minimal involvement → Promotion
41
-
Referral with monitoring → Case management
41
-
Structured human support → Coaching
41
-
Therapist-led integration → Symptom-focused therapy
41
-
Advanced multi-modal integration → Comprehensive therapy
41
-
12
Ramos, G., & Chavira, D. A. (2022). Use of technology to provide mental health care for racial and ethnic minorities: evidence, promise, and challenges. Cognitive and Behavioral Practice, 29(1), 15-40. https://doi.org/10.1016/j.cbpra.2019.10.004
42
-
1. Core Argument of the Paper
42
-
Unequal distribution of providers
42
-
Logistical barriers (transportation, work schedules, childcare)
42
-
Attitudinal barriers (stigma, mistrust, language barriers, fear of discrimination)
42
-
2. Do BITs Improve Access?
42
-
3. The Five Clinical Recommendations
42
-
Whether the BIT is appropriate for the presenting problem.
43
-
Whether it supports treatment goals.
43
-
Whether additional therapist monitoring is needed.
43
-
Access to devices (phone, computer, tablet)
43
-
Internet reliability
43
-
Ability to afford connectivity
43
-
Comfort and familiarity with technology
43
-
Effectiveness of digital therapy
43
-
Privacy
43
-
Feeling that technology is impersonal
43
-
Beliefs that therapy must be face-to-face to work
43
-
Data security concerns
44
-
Fear of surveillance
44
-
Mistrust of institutions
44
-
Use secure platforms
44
-
Explain confidentiality clearly
44
-
Ensure compliance with privacy regulations
44
-
Consider whether additional monitoring or safety planning is required (e.g., in cases of suicidality)
44
-
4. Overall Position of the Authors
44
-
Logistical barriers
44
-
Geographic barriers
44
-
Some stigma-related barriers
44
-
Structural racism
44
-
Socioeconomic inequalities
44
-
Cultural mistrust
44
-
Digital divides
44
-
13
Subject 3; current state of technology
45
-
14
Bauducco, S., Pillion, M., Bartel, K., Reynolds, C., Kahn, M., & Gradisar, M. (2024). A bidirectional model of sleep and technology use: a theoretical review of how much, for whom, and which mechanisms. Sleep Medicine Reviews, 76, 101933. https://doi.org/10.1016/j.smrv.2024.101933
45
-
1. From a Unidirectional to a Bidirectional Model
45
-
2. The Four Established Pathways (Technology → Sleep)
45
-
3. Reverse Mechanisms (Sleep → Technology)
46
-
4. Moderators: For Whom Is Technology Harmful?
46
-
5. Clinical Implications: Beyond “No Screens Before Bed”
46
-
6. Integration with Self-Regulation and Digital Behavior
47
-
15
Klein Tuente, S., Bogaerts, S., Bulten, E., Keulen-de Vos, M., Vos, M., Bokern, H., … Veling, W. (2020). Virtual Reality Aggression Prevention Therapy (VRAPT) versus Waiting List Control for Forensic Psychiatric Inpatients: A Multicenter Randomized Controlled Trial. Journal of Clinical Medicine, 9(7), 2258. https://doi.org/10.3390/jcm9072258
48
-
Why is VR potentially very interesting for aggression prevention therapy? (3 reasons)
48
-
What were the outcomes?
49
-
What is the role of the therapist in VRAPT?
49
-
Two interesting follow-up studies (based on the discussion)
50
-
Overall takeaway for essay questions
50
-
16
Hanley, T., Prescott, J., & Gomez, K. U. (2019). A systematic review exploring how young people use online forums for support around mental health issues. Journal of Mental Health, 28(5), 566–576. https://doi.org/10.1080/09638237.2019.1630725
51
-
Why do young people still use forums, and what does that teach us for new interventions?
52
-
Strategic lesson for new technology-based interventions:
52
-
Opportunities for professionals (3 themes)
52
-
Challenges for professionals (3 themes)
53
-
Bringing it together for essay questions (what you should be able to argue)
54
-
17
Josephine, K., Josefine, L., Philipp, D., David, E., & Harald, B. (2017). Internet- and mobile-based depression interventions for people with diagnosed depression: A systematic review and meta-analysis. Journal of Affective Disorders, 223, 28–40. https://doi.org/10.1016/J.JAD.2017.07.021
54
-
1. Why is this study unique?
54
-
2. Research Questions
55
-
3. Main Results
55
-
4. What does the Discussion emphasize?
56
-
6. What you should argue in an open essay question
57
-
18
Koelen, J., Klein, A., Wolters, N., Bol, E., De Koning, L., Roetink, S., ... & Wiers, R. (2024). Web-Based, Human-Guided, or Computer-Guided Transdiagnostic Cognitive Behavioral Therapy in University Students With Anxiety and Depression: Randomized Controlled Trial. JMIR Mental Health, 11, e50503. doi: 10.2196/50503
58
-
The Three Conditions
58
-
Delivery and Ethical Safeguards
59
-
Main Findings
59
-
Adherence and Engagement
60
-
Interpretation in the Discussion
60
-
19
Subject 4; Innovative technology and new ideas in mental healthcare
61
-
20
Bond, R. R., Mulvenna, M. D., Potts, C., O’Neill, S., Ennis, E., & Torous, J. (2023). Digital transformation of mental health services. Npj Mental Health Research, 2(1), 13. https://doi.org/10.1038/s44184-023-00033-y
61
-
1. What is the article about?
61
-
2. Types of Digital Mental Health Technologies
62
-
4. Functions of Digital Mental Health
63
-
5. Six Benefits of Digital Interventions
63
-
6. Four Challenges of Digital Interventions
64
-
7. Stakeholder-Centred Design
64
-
What are “Therapeutic Process Factors”?
65
-
Why are Therapeutic Process Factors Important in Digital Mental Health?
65
-
How Were Therapeutic Process Factors Implemented in Woebot?
66
-
Conceptual Contribution of the Article
67
-
What You Should Be Able to Explain for an Essay
67
-
1. Emotion Regulation as the Core Concept
68
-
2. The Autonomic Nervous System (ANS)
68
-
3. What is Biofeedback?
69
-
4. Traditional vs. Ambulatory Biofeedback vs. Biocueing
69
-
5. Study Aim
70
-
6. Main Findings
70
-
7. Feasibility
70
-
8. Limitations of the Evidence
71
-
9. Future Directions
71
-
21
Mohr, D. C., Zhang, M. & Schueller, S. M. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annu. Rev. Clin. Psychol. 13, 23–47 (2017). https://doi-org.ezproxy2.utwente.nl/10.1146/annurev-clinpsy-032816-044949
71
-
1. What is “Personal Sensing” and Why Is It Important?
71
-
2. From Raw Data to Clinical Meaning: The Hierarchical Model (Figure 1)
72
-
3. Machine Learning: What It Actually Means
73
-
4. What Has Research Actually Achieved?
73
-
5. Why We Must Be Cautious
74
-
6. What Can Mental Healthcare Realistically Expect in the Next 2–3 Years?
75
-
22
Bossenbroek, R., Wols, A., Weerdmeester, J., Lichtwarck-Aschoff, A., Granic, I., & van Rooij, M. M. (2020). Efficacy of a virtual reality biofeedback game (DEEP) to reduce anxiety and disruptive classroom behavior: Single-case study. JMIR Mental Health, 7(3), e16066. https://doi.org/10.2196/16066
75
-
What is the technological intervention and why is it appropriate for young individuals?
75
-
Why is DEEP particularly appropriate for young individuals in special education?
76
-
2️. Why use a Single-Case Experimental Design (SCED)?
77
-
Main Clinical Implications
78
-
Clinical implication for special schools
79
-
23
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 1-17. https://doi-org.ezproxy2.utwente.nl/10.1007/s12160-016-9830-8
80
-
What is a JITAI?
80
-
The Core Components of a JITAI (Figure 1)
80
-
3️ Engagement and Adherence
81
-
4️ Critical Perspective and Challenges
82
-
5️ Big Conceptual Take-Home Message
83
-
What You Should Be Able to Do in an Essay
83
-
24
Blease, C., & Torous, J. (2023). ChatGPT and mental healthcare: balancing benefits with risks of harms. BMJ Ment Health, 26(1). https://doi.org/10.1136/bmjment-2023-300884
83
-
25
Sikström, S., Boehme, R. A., Mirström, M., Agbotsoka, T., Győri, G., Lasota, M., Tabesh, M., Stille, L., & Garcia, D. (2025). Generative AI-assisted clinical interviewing of mental health. Scientific Reports, 15(1), 37737. https://doi.org/10.1038/s41598-025-13429-x
85
-
What the paper shows
86
-
What the paper does not prove
86