A qualitative study of a blended therapy using problem solving therapy with a customised smartphone app in men who present to hospital with intentional self-harm
Bibliographic record
Abstract
BACKGROUND: Blended therapy describes the use of computerised therapy combined with face-to-face therapy to extend the depth, range and nature of the face-to-face therapy. We wanted to develop a treatment manual for a randomised trial of blended therapy combining face-to-face problem solving and a smartphone app in men who present to hospital with self-harm. OBJECTIVE: To develop a treatment manual and to describe the experience of receiving and delivering a blended therapy. METHODS: After completion of the blended therapy, semistructured qualitative interviews were conducted with participants to describe their experience of the treatment. Two independent coders analysed the material using a thematic, grounded theory approach. FINDINGS: Seven men were enrolled in the study, and six completed the qualitative interviews. The two main themes identified were of trust and connection. Participants attended 85% of their appointments. CONCLUSIONS: In the treatment manual, we emphasised the themes of trust and connection by allowing time to discuss the app in the face-to-face to sessions, ensuring that therapists are familiar with the app and know how to respond to technical queries. Identification of trust and connection generates novel questions about the importance of the therapeutic alliance with technology rather than with people. CLINICAL IMPLICATIONS: Clinicians and app developers need to pay attention to the therapeutic relationship with technology as trust and good communication can be easily damaged, resulting in disengagement with the app. Blended therapy may result in increased adherence to face-to-face sessions. TRIAL REGISTRATION NUMBER: NCT02718248.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".