Blending Cognitive Analytic Therapy With a Digital Support Tool: Mixed Methods Study Involving a User-Centered Design of a Prototype App
Bibliographic record
Abstract
BACKGROUND: Patients can struggle to make good use of psychotherapy owing to deficits in awareness, and digital technologies that support awareness are at a premium. Currently, when patients participate in cognitive analytic therapy (CAT), the technology supporting relational awareness work involves completion of paper-based worksheets as between-session tasks. OBJECTIVE: We aimed to design, with therapists and patients, a prototype digital mobile app. This was to help patients better engage in the "recognition" phase of the CAT treatment model by providing an unobtrusive means for practicing relational awareness with dynamic feedback on progress. METHODS: A national online survey was conducted with CAT therapists (n=50) to determine readiness for adoption of a mobile app in clinical practice and to identify core content, functionality, and potential barriers to adoption. A prototype mobile app based on data and existing paper-based worksheets was built. Initial face-to-face user testing of the prototype system was completed with three therapists and three CAT expatients. RESULTS: Among the therapists surveyed, 72% (36/50) reported not currently using any digital tools during CAT. However, the potential value of a mobile app to support patient awareness was widely endorsed. Areas of therapist concern were data security, data governance, and equality of access. These concerns were mirrored during subsequent user testing by CAT therapists. Expatients generated additional user specifications on the design, functionality, and usability of the app. Results from both streams were integrated to produce five key changes for the reiteration of the app. CONCLUSIONS: The user-centered design process has enabled a prototype CAT-App to be developed to enhance the relational awareness work of CAT. This means that patients can now practice relational awareness in a much more unobtrusive manner and with ongoing dynamic feedback of progress. Testing the acceptability and feasibility of this technological innovation in clinical practice is the next stage in the research process, which has since been conducted and has been submitted. The important challenges of data protection and governance must be navigated in order to ensure implementation and adoption if the CAT-App is found to be acceptable and clinically effective.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".