Therapy in the digital age: exploring in-person and virtual cognitive behavioural therapy
Bibliographic record
Abstract
INTRODUCTION: The adoption of client-centred care has become a foundational principle in mental health treatment, prioritising interventions tailored to the unique needs and preferences of clients across settings. Virtual or internet-based cognitive behavioural therapy (eCBT) has emerged as an effective, cost-efficient alternative to traditional, in-person CBT for a variety of mental health conditions, including anxiety and depression. Initially explored in experimental settings, eCBT gained substantial use during COVID-19, when the demand for accessible, remote mental health services were needed. Despite its broad implementation, limited research exists on the real-world experiences of clients who have participated in eCBT, particularly regarding its strengths and challenges compared to in-person therapy. OBJECTIVES: This study aimed to (1) explore the experiences of clients who have participated in both in-person CBT and eCBT, and (2) identify strengths and challenges associated with each modality from the client's perspective. METHODS: Clients were recruited from three outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Whitby, Ontario. In-depth interviews were conducted with twelve clients between June and December 2023. Transcripts were analysed using Braun and Clarke's six-step approach to thematic analysis. RESULTS: Five main themes emerged from the data: (1) accessing therapy in a new way; (2) building a foundation for care: the client-provider relationship; (3) satisfaction with care; (4) addressing clients' needs in the environment; and (5) client empowerment. Many clients expressed high satisfaction with eCBT, citing factors such as ease of access, flexibility, and the perceived effectiveness of virtual sessions in fostering mental health support. However, clients also noted challenges with technology, which could impact therapeutic engagement and the quality of the client-provider relationship. DISCUSSION: The strengths and challenges identified in eCBT parallel those encountered in in-person settings, though eCBT was particularly appreciated by clients comfortable with digital environments. These findings emphasise the importance of client-centred care in virtual contexts, including the need for provider training in digital rapport-building and consideration of technological barriers. Ultimately, insights from this study can inform the refinement of eCBT delivery and support tailored approaches that align with the diverse needs of mental health service users in post-pandemic healthcare landscapes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".