Insomnia and caregiver burden in chronic pain patients: A cross-sectional clinical study
Bibliographic record
Abstract
Insomnia is a major comorbid symptom of chronic pain and is likely to affect caregiver burden. This cross-sectional study investigated the association between insomnia in chronic pain patients and family caregiver burden. Participants were 60 patients with chronic pain of ≥3 months duration. Demographic and clinical information were collected using the Athens Insomnia Scale (AIS), the Pain Disability Assessment Scale (PDAS), the Hospital Anxiety and Depression Scale (HADS), and a pain intensity numerical rating scale (NRS). Family members who accompanied chronic pain patients to hospital completed the Zarit Burden Interview (ZBI). Univariate regression analysis and multiple regression analysis were conducted to clarify the associations between ZBI scores and total/subscale AIS scores. Covariates were age; sex; pain duration; and scores on the PDAS, HADS anxiety subscale, HADS depression subscale, and NRS. Insomnia was independently associated with ZBI scores [β: 0.27, 95% confidence interval (CI): 0.07-0.52, p = 0.001]. Scores on the AIS subscale of physical and mental functioning during the day were significantly associated with ZBI scores (β: 0.32, 95% CI: 0.05-0.59, p = 0.007). In conclusion, the findings suggest that in chronic pain patients, comorbid insomnia and physical and mental daytime functioning is associated with family caregiver burden independently of pain duration, pain-related disability, and pain intensity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".