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Record W1985265451 · doi:10.1080/16506073.2011.632434

Can We Modify Maladaptive Attributions for Fatigue?

2012· article· en· W1985265451 on OpenAlexaff
Andrea L. Harris, Colleen E. Carney

Bibliographic record

VenueCognitive Behaviour Therapy · 2012
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAttributionAnxietyIntervention (counseling)PsychologyInsomniaSleep (system call)Clinical psychologySleep disorderPsychiatrySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Research has shown that those with insomnia focus primarily on their sleep as a cause of daytime fatigue rather than the multitude of other possible causes of fatigue. This can create sleep-related anxiety and further perpetuate the sleep disturbance. In order to lessen the increased focus on sleep, the present study investigated whether people could learn to consider other attributions for fatigue via an information-based manipulation. Undergraduate students (N = 88) were randomized to two information groups: They either received information about common factors that could explain daytime fatigue (the fatigue information condition) or received generic sleep-related information (the control condition). Each group was tested pre- and post-intervention. Fatigue information participants were significantly more likely to consider non-sleep-related attributions for fatigue at post-intervention, relative to control participants. These results demonstrate that attributions for fatigue may be amenable to change via an information-based intervention; thus, this research explores a preliminary step toward investigating refinements to insomnia treatments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.109
GPT teacher head0.371
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2012
Admission routes1
Has abstractyes

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