Examining the Utility of a Sleep Resource in Transdiagnostic Internet-Delivered Cognitive Behavior Therapy: An Observational Study
Bibliographic record
Abstract
Patients seeking transdiagnostic internet-delivered cognitive behavior therapy (T-ICBT) for anxiety or depression often have sleep difficulties. A brief resource that includes sleep psychoeducation and strategies for improving sleep (e.g., stimulus control and sleep restriction) may address comorbid insomnia without the need for an insomnia-specific ICBT course. This observational study explored patient use and feedback of a brief sleep resource available to all patients (n = 763) enrolled in an 8-week T-ICBT course. Overall, 30.1% of patients (n = 230) reviewed the resource and were older, more engaged with the ICBT course (i.e., more likely to complete the program, more logins, and greater number of days enrolled in the course) and had higher pretreatment insomnia symptoms than those who did not review the resource. Resource reviewers did not report larger improvements in symptoms of insomnia than non-reviewers, even among patients with clinical levels of insomnia, and average insomnia levels remained above the clinical cutoff at posttreatment. While patients were satisfied with the resource and it was beneficial to some patients, more research is needed to further explore how it may be integrated into T-ICBT and how therapists can encourage the use of the resource among patients who may benefit from the resource.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".