Predictors of the effect of cognitive behavioral therapy for chronic insomnia comorbid with breast cancer.
Bibliographic record
Abstract
Prior studies have supported the efficacy of cognitive behavioral therapy (CBT) for insomnia comorbid with cancer. This article reports secondary analyses that were performed on one of these studies to investigate the predictive role of changes in dysfunctional beliefs about sleep, adherence to behavioral strategies, and some nonspecific factors on sleep changes assessed subjectively and objectively. Fifty-seven women with chronic insomnia comorbid with breast cancer received CBT for insomnia. At posttreatment, subjective sleep improvements were best predicted by higher initial levels of treatment expectancies, but also by decreased dysfunctional beliefs about sleep; the most consistent predictors of polysomnography (PSG) assessed sleep improvements were reduced dysfunctional beliefs about sleep and a higher avoidance of day napping. At 6-month follow-up, subjectively assessed sleep improvements were best predicted by adherence to behavioral strategies, whereas none of the predictors was significantly associated with PSG-assessed sleep improvements. This study gives some support to the importance of targeting erroneous beliefs about sleep and poor sleep habits in the treatment of cancer-related insomnia, but also to the importance of enhancing patients' expectancies for improvement.
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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 | 0.002 | 0.010 |
| 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 | 0.000 | 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".