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Record W2579574847 · doi:10.1002/jclp.22430

Client Retrospective Accounts of Corrective Experiences in Motivational Interviewing Integrated With Cognitive Behavioral Therapy for Generalized Anxiety Disorder

2017· article· en· W2579574847 on OpenAlexafffund
Christianne Macaulay, Lynne Angus, Jasmine Khattra, Henny A. Westra, Jennifer Ip

Bibliographic record

VenueJournal of Clinical Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsPsychologyWorryMotivational interviewingAnxietyCognitive behavioral therapyPsychotherapistCognitionGeneralized anxiety disorderInterviewGrounded theoryCognitive therapyClinical psychologyAnxiety disorderQualitative researchPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

A corrective experience (CE) is one "in which a person comes to understand or experience affectively an event or relationship in a different and unexpected way" (Castonguay & Hill, 2012, p. 5). CEs disconfirm clients' expectations based on past problematic experiences, and can be emotional, relational, behavioral, and/or cognitive. This qualitative study explored corrective shifts among recovered participants (N = 8) who had received motivational interviewing (MI) integrated with cognitive behavioral therapy (CBT) in a randomized controlled trial comparing CBT alone to MI-CBT for generalized anxiety disorder (Westra, Constantino, & Antony, 2016). We administered a posttherapy interview querying their experience of, and explanations for, any shifts in therapy. Grounded theory analysis yielded three core themes: in command of the worry train, experiencing myself in new ways in therapy, and oriented toward change. Findings are discussed in terms of MI theory, and clinical implications for therapists are provided.

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)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.237
GPT teacher head0.562
Teacher spread0.324 · 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 designQualitative
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

Citations15
Published2017
Admission routes2
Has abstractyes

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