Sleeping well with cancer: a systematic review of cognitive behavioral therapy for insomnia in cancer patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it