Understanding Treatment Response In CBT-I: Experiential And Theoretical Perspectives
Bibliographic record
Abstract
Cognitive behavioural therapy for insomnia (CBT-I) is empirically supported as efficacious and is considered the first line treatment for insomnia. However, it is not clear which specific components of CBT-I are most strongly associated with most improved outcome, and for whom the treatment is most effective. The present study examined participants’ experiences with CBTI in terms of the treatment components that they found to be most helpful. Participants with chronic insomnia (N = 163) completed 4 sessions of in-person CBT-I in a clinical trial. At the end of treatment, participants completed “letters” to their future selves including reminders of which elements of treatment they found to be most helpful; these letters were analyzed from a middle range theory approach using qualitative analyses. Although some studies have investigated moderators and mediators of CBT-I, these studies are few and sparse, and there are a variety of methodological problems. In response to the demand for an investigation of treatment mechanisms in CBT-I, the current study investigated a number of presumed theoretical treatment mechanisms on CBT-I outcomes via mediational analyses. The relationship between pre- and posttreatment insomnia severity was expected to be mediated by a number variables based factors presumed to perpetuate insomnia in the long-term. Qualitative analysis yielded eleven themes of treatment components that participants found to be most helpful. The eleven themes reflected the theoretical tenets of CBT-I, but also showed that self-efficacy, which is not currently prioritized in the CBT-I literature as a significant factor impacting treatment, to be important. Results from the quantitative analysis showed that sleep compression partially mediated prospectively measured insomnia severity at posttreatment, while changes in sleep related safety behaviours mediated self-reported insomnia severity. Findings from this study show that from their own perspective, clients confirm that the theoretical treatment factors upon which CBT-I is based are indeed helpful, and also that additional, less widely studied variables related to CBT-I, therapy more generally, and broader client concerns are important. Implications from this research include important considerations for clinicians delivering CBT-I, and potential new avenues for future research directions.
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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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| 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 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".