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Record W2432861049 · doi:10.1093/pm/pnw118

Pain Extent, Pain Intensity, and Sleep Quality in Adolescents and Young Adults

2016· article· en· W2432861049 on OpenAlexafffund
Rocío de la Vega, Mélanie Racine, Elisabet Sánchez‐Rodríguez, Catarina Tomé‐Pires, Elena Castarlenas, Mark P. Jensen, Jordi Miró

Bibliographic record

VenuePain Medicine · 2016
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchComunidad de Madrid
KeywordsMedicineSleep qualityYoung adultPain catastrophizingIntensity (physics)Sleep (system call)Pain medicinePhysical therapyChronic painPsychiatryGerontologyInsomnia

Abstract

fetched live from OpenAlex

OBJECTIVES: Pain has been shown to be associated with poor sleep quality. The aim of this study was to better understand the role that pain intensity and pain extent (number of painful areas) may play in the sleep quality of young people with acute and chronic pain. DESIGN: Cross-sectional survey. SETTING AND PATIENTS: A convenience sample of adolescents and young adults with acute or chronic pain; 414 individuals ages 12 to 24 (44% with chronic pain). METHODS: We performed a hierarchical regression analysis with sleep as the dependent variable and pain intensity, extent, age and pain chronicity as predictors. RESULTS: Pain extent and pain intensity made significant and independent contributions to the prediction of sleep quality (βs = 0.23 [P < 0.001] and 0.14 [P < 0.01]). Young adults reported poorer sleep than adolescents (β = 0.13, P < 0.01). Two significant interactions emerged: age × intensity (β = 0.39, P < 0.05) and chronicity × intensity (β = 0.88, P < 0.001). CONCLUSIONS: Sleep quality in young people could be improved by teaching them strategies to better manage pain intensity and pain extent. Clinical trials to evaluate the efficacy of (and best timing for) pain interventions to improve sleep quality are warranted.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
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 integrity0.0000.000
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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2016
Admission routes2
Has abstractyes

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