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Record W3158486619 · doi:10.1155/2021/5546484

The Pittsburgh Sleep Quality Index: Reliability, Factor Structure, and Related Clinical Factors among Children, Adolescents, and Young Adults with Chronic Pain

2021· article· en· W3158486619 on OpenAlexafffund
Cynthia L. Larche, Isabelle Plante, Mathieu Roy, Pablo Ingelmo, Catherine E. Ferland

Bibliographic record

VenueSleep Disorders · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMcGill University Health CentreMontreal Children's HospitalMcGill UniversityUniversité du Québec à MontréalShriners Hospitals for Children - Canada
FundersLouise and Alan Edwards Foundation
KeywordsPittsburgh Sleep Quality IndexConfirmatory factor analysisChronic painReliability (semiconductor)Exploratory factor analysisClinical psychologyPsychologyPopulationSleep qualityPhysical therapyMedicinePsychometricsPsychiatryStructural equation modelingInsomnia

Abstract

fetched live from OpenAlex

This study is aimed at assessing the psychometric properties and the factorial structure of the Pittsburgh Sleep Quality Index (PSQI) in a clinical sample of children, adolescents, and young adults with chronic pain. Data of 482 participants (aged 8-21 years) from two crosssectional studies and a chronic pain services outpatient clinic were analyzed. Exploratory and confirmatory factor analysis and reliability analysis of PSQI component scores were performed. Relationships between the PSQI global score and various clinical measures were investigated to assess external validity. The findings exhibit the reliability and validity of a single-factor model of the PSQI in a clinical sample of youth with chronic pain and support the relationship in this specific population between poor sleep quality and important clinical measures of well-being. These results support an informed decision regarding its use with this specific population and underscore the clinical relevance of assessing sleep quality.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.273
Teacher spread0.266 · 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

Citations52
Published2021
Admission routes2
Has abstractyes

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