Sleeping well with cancer: a systematic review of cognitive behavioral therapy for insomnia in cancer patients
Bibliographic record
Abstract
Individuals with cancer are disproportionately affected by sleep disturbance and insomnia relative to the general population. These problems can be a consequence of the psychological, behavioral, and physical effects of a cancer diagnosis and treatment. Insomnia often persists for years and, when combined with already high levels of cancer-related distress, may place cancer survivors at a higher risk of future physical and mental health problems and poorer quality of life. The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBT-I), a non-pharmacological treatment that incorporates cognitive and behavior-change techniques and targets dysfunctional attitudes, beliefs, and habits involving sleep. This article presents a comprehensive review of the literature examining the efficacy of CBT-I on sleep and psychological outcomes in cancer patients and survivors. The search revealed 12 studies (four uncontrolled, eight controlled) that evaluated the effects of CBT-I in cancer patients or survivors. Results suggest that CBT-I is associated with statistically and clinically significant improvements in subjective sleep outcomes in patients with cancer. CBT-I may also improve mood, fatigue, and overall quality of life, and can be successfully delivered through a variety of treatment modalities, making it possible to reach a broader range of patients who may not have access to more traditional programs. Future research in this area should focus on the translation of evidence into clinical practice in order to increase awareness and access to effective insomnia treatment in cancer care.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".