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Record W2034529127 · doi:10.1080/07448481.2011.630703

Evaluating a Web-Based Cognitive-Behavioral Therapy for Maladaptive Perfectionism in University Students

2012· article· en· W2034529127 on OpenAlexafffund
Natasha Radhu, Zafiris J. Daskalakis, Chantal A. Arpin‐Cribbie, Jane Irvine, Paul Ritvo

Bibliographic record

VenueJournal of American College Health · 2012
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsYork UniversityLaurentian UniversityUniversity of TorontoCentre for Addiction and Mental Health
FundersYork University
KeywordsPerfectionism (psychology)AnxietyClinical psychologyCognitive behavioral therapyPsychologyDepression (economics)CognitionAnxiety sensitivityPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study assessed a Web-based cognitive-behavioral therapy (CBT) for maladaptive perfectionism, investigating perfectionism, anxiety, depression, negative automatic thoughts, and perceived stress. PARTICIPANTS: Participants were undergraduate students defined as maladaptive perfectionists through a screening questionnaire at an urban university. The data were collected from July 2009 to August 2010. METHODS: Forty-seven maladaptive perfectionists were randomly assigned to a 12-week CBT or a wait-list control group and assessed via questionnaires at pre- and postintervention. Statistical procedures included t tests, Pearson correlations, and analysis of covariance. RESULTS: At the postintervention measure, the CBT group demonstrated significant decreases in anxiety sensitivity and negative automatic thoughts compared to the control group. Within the CBT group, changes in perfectionism scores were significantly correlated with positive changes in depression, anxiety, stress, and automatic thoughts. CONCLUSIONS: The treatment group improved on psychological outcomes, demonstrating the effectiveness of a Web-based CBT for perfectionism in a university setting.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.099
GPT teacher head0.452
Teacher spread0.354 · 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 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

Citations96
Published2012
Admission routes2
Has abstractyes

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Same venueJournal of American College HealthSame topicPerfectionism, Procrastination, Anxiety StudiesFrench-language works237,207