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Record W3115508427 · doi:10.1017/s1478951520001017

Feasibility and acceptability of cognitive behavioral therapy for insomnia (CBT-I) or acupuncture for insomnia and related distress among cancer caregivers

2020· article· en· W3115508427 on OpenAlexaff
Allison J. Applebaum, Kara Buda, Michael A. Hoyt, Kelly M. Shaffer, Sheila N. Garland, Jun J. Mao

Bibliographic record

VenuePalliative & Supportive Care · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMemorial University of Newfoundland
FundersNational Cancer InstituteNational Institutes of Health
KeywordsInsomniaCognitive behavioral therapy for insomniaDistressMedicineAcupuncturePsychological interventionCognitionCognitive behavioral therapyIntervention (counseling)Cognitive therapyRandomized controlled trialClinical psychologyPhysical therapyAlternative medicinePsychiatryPsychotherapistPsychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Insomnia is a common, distressing, and impairing psychological outcome experienced by informal caregivers (ICs) of patients with cancer. Cognitive behavioral therapy for insomnia (CBT-I) and acupuncture both have known benefits for patients with cancer, but such benefits have yet to be evaluated among ICs. The purpose of the present study was to evaluate the feasibility, acceptability and preliminary effects of CBT-I and acupuncture among ICs with moderate or greater levels of insomnia. METHOD: Participants were randomized to eight sessions of CBT-I or ten sessions of acupuncture. RESULTS: Results highlighted challenges of identifying interested and eligible ICs and the impact of perception of intervention on retention and likely ultimately outcome. SIGNIFICANCE OF THE RESULTS: Findings suggest preliminary support for non-pharmacological interventions to treat insomnia in ICs and emphasize the importance of matching treatment modality to the preferences and needs of ICs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.386
Teacher spread0.323 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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