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Record W1998160991 · doi:10.4236/health.2013.511240

Preferences for behavioral therapies for chronic insomnia

2013· article· en· W1998160991 on OpenAlexafffund
Sarah Ibrahim, Souraya Sidani

Bibliographic record

VenueHealth · 2013
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Metropolitan UniversityMcMaster University
FundersCanadian Institutes of Health Research
KeywordsInsomniaNoveltyChronic insomniaMedicineSleep restrictionClinical psychologyPreferenceStimulus controlStimulus (psychology)Physical therapySleep disorderPsychologyPsychiatryPsychotherapistSleep deprivationSocial psychologyCognition

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was twofold: to examine the acceptability and preference for the two behavioral therapies, and to identify factors persons with chronic insomnia take into account when choosing treatment. Methods: The data were obtained in a large trial evaluating the effects of Stimulus Control and Sleep Restriction therapies. Prior to treatment, participants completed the treatment acceptability and preference (TAP) questionnaire, which described the Stimulus Control Therapy and the Sleep Restriction Therapy and requested participants to rate the acceptability of each treatment option before choosing one for the management of chronic insomnia. Open-ended questions were used to explore the factors that participants considered when making a choice. Results: Participants rated the Sleep Restriction Therapy as acceptable and 70.2% of participants preferred it over Stimulus Control Therapy. The factors that influenced participants’ choice related to the familiarity, previous personal experience, novelty, and suitability of the treatment. Conclusion: Persons have expressed a preference for treatments to manage chronic insomnia. Healthcare providers are in a position to provide relevant information about treatment options in order to help persons make informed treatment related decisions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.065
GPT teacher head0.394
Teacher spread0.329 · 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 designNot applicable
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

Citations7
Published2013
Admission routes2
Has abstractyes

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