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Effects of Constructive Worry, Imagery Distraction, and Gratitude Interventions on Sleep Quality: A Pilot Trial

2011· article· en· W1566219942 on OpenAlexaff
Nancy Digdon, Amy Koble

Bibliographic record

VenueApplied Psychology Health and Well-Being · 2011
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMacEwan University
Fundersnot available
KeywordsWorryGratitudeDistractionPsychologyIntervention (counseling)Psychological interventionGuided imagerySleep (system call)Clinical psychologyPsychotherapistAnxietyPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

Background: There is mounting empirical evidence that poor sleep compromises well‐being. Our study focused on university students who have persistent problems sleeping because their minds are racing with stimulating thoughts and worries. We evaluated three self‐help interventions (constructive worry, imagery distraction, and a gratitude intervention) which were disseminated by e‐mail. Methods: Forty‐one participants (32 females) were randomly assigned to an intervention. Daily measures of sleep and pre‐sleep worry and arousal were collected online during a baseline week followed by an intervention week. Results: Each intervention reduced worry and pre‐sleep arousal, and improved sleep compared to baseline. One intervention did not differ from the others. Participants rated the interventions as moderately helpful. Conclusions: E‐mailed self‐help versions of constructive worry, imagery distraction, or a gratitude intervention helped university students quiet their minds and sleep better. This mode of delivery is feasible for broad distribution and at universities without access to sleep clinicians.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.854

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)0.0000.000

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 head0.034
GPT teacher head0.368
Teacher spread0.334 · 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 designTheoretical or conceptual
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

Citations42
Published2011
Admission routes1
Has abstractyes

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