Advances in digital CBT: where are we now, and where next?
Bibliographic record
Abstract
Abstract Digital CBT refers to the use of digital tools, platforms or devices to deliver or enhance cognitive behavioural therapy assessment, formulation, treatment, training and supervision. The ‘Advances in Digital CBT’ special issue aimed to document examples of innovative digital CBT practice in this rapidly developing field. In this paper, we have briefly summarised and synthesised the advances demonstrated in this group of articles. These include developments in our understanding of mental health apps, the use of digital tools as an adjunct to therapy, the effectiveness of remotely delivered CBT in routine clinical practice, our understanding of user experiences and involvement, and in digital CBT research methods. We consider the extent of current knowledge in these areas and identify where gaps in evidence lie and how the field could be taken forward to address these. Lastly, we reflect on the broader digital CBT picture and offer our suggestions of six key directions for future research: using robust study designs to evaluate and optimise digital tools; translating and culturally adapting digital tools and practices; understanding and addressing digital exclusion; exploring, reporting and addressing possible negative effects; improving user involvement in design and evaluation; and addressing the implementation gap for digital tools. We suggest that further advances in these areas would be of particular benefit to the digital CBT field. Key learning aims (1) To gain an overview of the articles in the special issue and an understanding of the advances in digital CBT they represent. (2) To understand how the advances suggested by the present studies could be taken forward and extended. (3) To consider key future directions for further advances in digital CBT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".