The Mediating and Moderating Role of Sleep Disturbance Between Chronic Pain and Psychological Distress: Evidence from Canadian Community Health Survey
Bibliographic record
Abstract
Objective: Chronic pain can result in psychological distress. In addition, sleep disturbance is associated with both chronic pain and psychological distress. However, no study has comprehensively investigated the roles of sleep disturbance in the associations between pain and psychological distress in the general population. Our study aims to explore the mediating as well as the moderating effects of sleep disturbance between chronic pain and psychological distress in a national Canadian sample. Methods: Data were analyzed from the Canadian Community Health Survey-Mental Health (CCHS-MH), a national cross-sectional study comprised of adult respondents who provided information on chronic pain, sleep and psychological distress (N = 25,113). The 10-item Kessler Psychological Distress Scale (K-10) was used to assess psychological distress. Structural equation modeling (SEM) was used to measure the mediating and moderating effects of sleep disturbance on the relationships between chronic pain and psychological distress. Results: Our findings indicated that sleep disturbance had both direct and indirect effects on psychological distress. Sleep disturbance partially mediated the relationships between chronic pain and psychological distress (β = 0.10, p < .05). The model accounts for 30.3% of the variance in psychological distress. Sleep disturbance also played a moderating role in the relationships between chronic pain and psychological distress. The pronounced moderation effect was found in the “no sleep disturbance group” (β = 0.20, p < .05). Conclusions: These results revealed that addressing sleep problems should be one of the targets of intervention and prevention for psychological distress among those individuals suffering from chronic pain.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".