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Record W2158745527 · doi:10.1177/070674370204700905

Sleep Quality in Chronic Pain Patients

2002· article· en· W2158745527 on OpenAlexaffvenue
Kemal Sayar, Meltem Arıkan, Tulin Yontem

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2002
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexChronic painAnxietyBeck Depression InventoryMoodBeck Anxiety InventoryDepression (economics)Sleep disorderPopulationPhysical therapyMedicinePsychologyInsomniaPsychiatrySleep quality

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.017
GPT teacher head0.266
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations151
Published2002
Admission routes2
Has abstractyes

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