Is cognitive behavioural therapy for insomnia effective in treating insomnia and pain in individuals with chronic non-malignant pain?
Bibliographic record
Abstract
AIM: This paper systematically reviews clinical trials investigating the effectiveness of cognitive behavioural therapy for insomnia and pain in patients with chronic non-malignant pain. METHOD: A systematic search of MEDLINE, PSYCINFO, EMBASE, CINHAL and Cochrane library and register of trials was conducted. RESULTS: Essential components of cognitive behavioural therapy for insomnia were included in all studies except for the cognitive restructuring component, which was not considered an intervention in one study. Interventions were provided by adequately trained clinicians. Significant within-group effect sizes (> 1) were observed in the intervention groups as compared with the control groups. Improvements were noted in sleep latency, sleep efficiency and wake after sleep onset times. Although improvements were noted in pain experienced by the participants, this was not a significant finding. CONCLUSIONS: These clinical trials demonstrate that cognitive behavioural therapy for insomnia is effective as an intervention for insomnia in individuals suffering from chronic non-malignant pain. Although pain and disturbed sleep are linked, cognitive behavioural therapy for insomnia alone may not be an effective solution for addressing chronic non-malignant pain. Trials of cognitive behavioural therapy for insomnia on a variety of chronic pain patients with disturbed sleep and with long-term follow-up are required to ascertain whether cognitive behavioural therapy for insomnia is an effective intervention to reduce pain and to add to increasing evidence that it is an effective intervention for insomnia in the chronic pain population.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".