Bibliographic record
Abstract
OBJECTIVE: Chronic pain patients have been reported to complain about poor sleep quality. Research aimed at delineating the predictors of poor sleep has produced conflicting results. Depressive mood and pain severity are the most frequently encountered predictors. This study aimed to find out whether chronic pain patients differed from healthy control subjects who had no pain on subjective sleep quality measures and, if so, which factors contributed most to poorer sleep quality. METHOD: We compared 40 patients with chronic pain who met inclusion criteria with 40 healthy control subjects on the measures of sleep quality, anxiety, and depression. The predictors of sleep quality were investigated with multiple regression in the pain group. RESULTS: Chronic pain patients had higher scores than did healthy control subjects on the Beck Anxiety Scale, the Beck Depression Inventory (BDI), and the Pittsburgh Sleep Quality Index (PSQI). At the bivariate level, pain intensity, anxiety, and depression correlated significantly with poorer sleep quality. At the multivariate level, depression was found to be the only significant factor correlating with the quality of sleep, and the model explained 34% of the variance. CONCLUSIONS: Chronic pain patients suffer from poor sleep quality--a function of depressed mood rather than pain intensity, duration, or anxiety. However, it is difficult to draw a causal relation in this relatively small sample size. Besides, our study sample comprised a mostly psychiatric population and may not represent the general group of patients with chronic pain.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".