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Record W4241330284 · doi:10.32920/ryerson.14660592

Experimental Manipulation of Processing Style: Impact on Interpretive Bias, Problem Solving, and Worry in Individuals with Generalized Anxiety Disorder

2021· preprint· en· W4241330284 on OpenAlexafffund
Elizabeth Jane Pawluck

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsToronto Metropolitan UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsWorryGeneralized anxiety disorderStyle (visual arts)PsychologyAnxietyCognitive styleCognitionClinical psychologyCognitive biasCognitive psychologyOrientation (vector space)Psychiatry

Abstract

fetched live from OpenAlex

The present study investigated whether individuals with generalized anxiety disorder (GAD) could be trained to adopt an abstract or concrete processing style and the impact of processing style training on GAD symptoms and cognitive processes, including an interpretation bias, negative problem solving orientation, poor problem solving, and worry. Participants (N=47) were trained to adopt an abstract or concrete processing style, and outcome measures were completed at posttraining and 1 week follow-up. At posttraining, processing style training was effective in inducing an abstract or concrete processing style. In addition, at posttraining, the concrete training condition reported reduced concern with ambiguous scenarios and produced problem solutions that were rated as more effective compared with the abstract training condition. At follow-up, there was no difference between training conditions on processing style and associated GAD symptoms and cognitive processes. Study limitations and future directions are discussed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.360
Teacher spread0.321 · 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 designNon-randomized trial
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

Citations0
Published2021
Admission routes2
Has abstractyes

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