Do beliefs about treatment credibility and expectancy influence the outcomes of cognitive behavioral therapy for insomnia among cancer survivors
Bibliographic record
Abstract
Objectives: Cognitive Behavioral Therapy for Insomnia (CBT-I) effectively improves insomnia and perceived cognitive impairment (PCI) in cancer survivors. This prespecified secondary analysis assessed how credibility and expectancy beliefs influence insomnia and PCI outcomes, and explored what factors were associated with higher beliefs about credibility and expectancy. Methods: As part of a randomized waitlist-controlled trial, cancer survivors (N = 132) who met the DSM-5 criteria for insomnia disorder and reported PCI received 7 weekly virtual CBT-I sessions. Credibility and expectancy beliefs were assessed separately for insomnia and PCI using the Credibility Expectancy Questionnaire. We also examined whether credibility and expectancy moderated change in symptoms of insomnia and PCI, controlling for age. Factors associated with greater credibility and expectancy beliefs were evaluated using linear regression and qualitative interviews were used to explore patient perceptions. Results: Only younger age was associated with higher pre-treatment expectations for insomnia and PCI outcomes (p = 0.009; p = 0.008). Beliefs about credibility or expectancy did not moderate change in symptoms of insomnia (p = 0.972; p = 0.502) or PCI (p = .143; p = 0.283). Qualitative results suggest that skepticism and doubt, one’s understanding of sleep, and optimism and open-mindedness influence expectation of outcome and perceptions of treatment credibility. Conclusion/Implications: CBT-I has robust efficacy regardless of pre-existing beliefs and expectations. While these factors may play a role in the decision to pursue CBT-I, our results suggest that clients are likely to experience benefits if they sufficiently engage in the therapy. Treatment outcomes may be enhanced by: 1) providing psychoeducation about sleep; 2) setting appropriate pre-treatment beliefs that foster optimism; 3) promoting consistent engagement with treatment; and 4) fostering positive therapeutic relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".