MétaCan
Menu
Back to cohort
Record W1987749039 · doi:10.1002/jclp.20429

Client personality characteristics predict satisfaction with cognitive behavior therapy

2007· article· en· W1987749039 on OpenAlexaff
Sheryl M. Green, Thomas Hadjistavropoulos, Donald Sharpe

Bibliographic record

VenueJournal of Clinical Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of ReginaMcMaster University
Fundersnot available
KeywordsPsychologyAgreeablenessClinical psychologyNeuroticismCognitive therapyPsychosocialPersonalityCognitionPsychotherapistOpenness to experienceCognitive behavioral therapyBig Five personality traitsPsychiatryExtraversion and introversionSocial psychology

Abstract

fetched live from OpenAlex

Ratings of treatment satisfaction are a means for cognitive behavior therapy clients to provide their unique personal perspective on their therapy experience. Treatment satisfaction is a variable of growing importance as a predictor of outcome for various medical and psychological treatments including treatments for chronic pain (D. C. Turk et al., 2003). Our goal was to determine whether satisfaction with cognitive behavior therapy sessions varied as a function of patient personality characteristics in a sample of 43 older adults (average age=72.3 years, SD=8.0) participating in a psychosocial pain management therapy program with a cognitive behavioral orientation. Participants completed the NEO Five Factor Inventory (P. T. Costa, Jr. & R. R. McCrae, 1992) prior to the commencement of treatment and a psychometrically valid questionnaire, assessing satisfaction with psychological therapy, after each therapy session. The core personality dimensions of neuroticism, openness, and agreeableness were predictive of aspects of satisfaction with therapy. These findings have the potential of being useful to clinicians concerned with the prediction of response to therapy.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.164
GPT teacher head0.513
Teacher spread0.349 · 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.

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

Citations13
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical PsychologySame topicPersonality Disorders and PsychopathologyFrench-language works237,207