Acceptability and Feasibility of Training to Integrate Digital CBT for Insomnia Into Routine Psychotherapy: A Focus Group Study
Bibliographic record
Abstract
OBJECTIVE: Routine psychotherapy for mental health problems does not adequately address insomnia. Integrating cognitive behavior therapy for insomnia (CBTI) into routine psychotherapy could both extend the reach of CBTi and enhance sleep and mental health outcomes. Digital CBTi (dCBTI) is a promising and scalable option for integration that requires little prior training and session time. This study aimed to understand the perspectives of licensed mental health therapists on the acceptability and feasibility of this strategy of integrated dCBTI. METHOD: Six one-hour focus groups were conducted with 52 licensed therapists (21 PhD/PsyD, 11 LCSW, 10 MFT, 9 LPC, 2 MD). Each group included 6-11 participants. Inductive thematic analysis was used. RESULTS: Therapists identified general advantages of dCBTI, benefits to integration, and concerns about integration. They described the knowledge and resources needed both for training and in session. They expressed that a 4-hour workshop and spending 5-10 minutes in session supporting patient use of dCBTI would be feasible. CONCLUSION: Data offer preliminary evidence in support of the perceived value, acceptability, and feasibility of integrating dCBTI in routine psychotherapy from a therapist perspective. Therapists are open to receiving training in integrated dCBTI and see its potential value in improving outcomes for their patients.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".