Cognitive-behavioral, behavioural and mindfulness-based therapies for insomnia in menopause
Bibliographic record
Abstract
OBJECTIVES: Insomnia is frequently reported by women during menopause due to physiological changes and environmental factors and is associated with negative daytime sequelae. Due to medication side effects and patient preferences, there is increased interest in the use of psychological treatments for menopausal insomnia. The primary objective of this review is to review the efficacy of cognitive-behavioral, behavioral, and mindfulness-based (CBBMB) therapies in treating insomnia in peri- and post-menopausal women. The secondary objective is to review the effect of CBBMB therapies on relevant secondary outcomes to gain a comprehensive understanding of their impacts. METHODS: We conducted a narrative review of the literature. A search of PubMed and Google Scholar was conducted between January 2020 and March 2021. RESULTS: Cognitive-behavioral therapy (CBT) for insomnia is efficacious, with corollary improvements in mood, functional outcomes and potential mechanistic factors (e.g., unhelpful beliefs). Sleep restriction therapy is also efficacious, with somewhat poorer effects on secondary outcomes relative to CBT. Mindfulness meditation and relaxation for insomnia demonstrated promise, but its long-term effects remain unknown. CONCLUSIONS: Research with more diverse samples and head-to-head comparisons is needed. Dissemination of CBBMBs for insomnia in clinics where menopausal women seek care is an important next step.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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".