A brief cognitive–behavioral intervention for sleep in individuals with chronic noncancer pain: A randomized controlled trial.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Chronic pain has a significant negative impact on the quality of life, including sleep disruption. There is compelling evidence that cognitive-behavioral therapy can be effective in treating sleep disorders. To our knowledge, no research has been carried out on brief cognitive-behavioral educational interventions in individuals with chronic pain. This study was conducted to determine whether a brief education session that incorporates sleep hygiene and cognitive-behavioral strategies would help improve the sleep of individuals with chronic pain. RESEARCH METHOD/DESIGN: Eighty-five patients from a tertiary care Multidisciplinary Pain Centre completed all aspects of the study. This sample was randomized into 2 groups: a treatment group who received a brief cognitive-behavioral educational session, and a control group who did not. All participants completed a daily sleep diary for 28 days. Measures on sleep quality, beliefs and attitudes about sleep, pain, disability, and mood were recorded at baseline. RESULTS: No significant differences were found between groups on demographic, pain, disability, mood measures, or sleep quality at baseline. Overall, 42% of the individuals who completed this study had depression scores above the clinical cutoff. This sample reported a high level of pain-related disability. Individuals in the treatment group had significantly reduced sleep onset latency compared to controls. No significant differences between groups on the number of times waking or hours slept. CONCLUSIONS/IMPLICATIONS: Our findings suggest that there is potential for a brief educational intervention to have a positive impact on some aspects of sleep 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".