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Record W2059539734 · doi:10.1037/a0015492

Predictors of the effect of cognitive behavioral therapy for chronic insomnia comorbid with breast cancer.

2009· article· en· W2059539734 on OpenAlexafffund
Valérie Tremblay, Josée Savard, Hans Ivers

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsDysfunctional familyInsomniaCognitive behavioral therapy for insomniaPolysomnographyPsychologyClinical psychologyCognitionBreast cancerSleep disorderSleep (system call)Cognitive behavioral therapyActigraphyPsychiatryMedicineCancerInternal medicine

Abstract

fetched live from OpenAlex

Prior studies have supported the efficacy of cognitive behavioral therapy (CBT) for insomnia comorbid with cancer. This article reports secondary analyses that were performed on one of these studies to investigate the predictive role of changes in dysfunctional beliefs about sleep, adherence to behavioral strategies, and some nonspecific factors on sleep changes assessed subjectively and objectively. Fifty-seven women with chronic insomnia comorbid with breast cancer received CBT for insomnia. At posttreatment, subjective sleep improvements were best predicted by higher initial levels of treatment expectancies, but also by decreased dysfunctional beliefs about sleep; the most consistent predictors of polysomnography (PSG) assessed sleep improvements were reduced dysfunctional beliefs about sleep and a higher avoidance of day napping. At 6-month follow-up, subjectively assessed sleep improvements were best predicted by adherence to behavioral strategies, whereas none of the predictors was significantly associated with PSG-assessed sleep improvements. This study gives some support to the importance of targeting erroneous beliefs about sleep and poor sleep habits in the treatment of cancer-related insomnia, but also to the importance of enhancing patients' expectancies for improvement.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.443
Teacher spread0.406 · 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

Citations68
Published2009
Admission routes2
Has abstractyes

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