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Record W2755052429 · doi:10.1002/jclp.22527

Using interpersonal process recall to compare patients’ accounts of resistance in two psychotherapies for generalized anxiety disorder

2017· article· en· W2755052429 on OpenAlexafffund
Nicholas R. Morrison, Michael J. Constantino, Henny A. Westra, Angela Kertes, Brien J. Goodwin, Martin M. Antony

Bibliographic record

VenueJournal of Clinical Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsToronto Metropolitan UniversityYork University
FundersCanadian Institutes of Health Research
KeywordsPsychologySuperordinate goalsResistance (ecology)PsychotherapistClinical psychologyNarrativeAmbivalenceInterpersonal communicationRecallConceptualizationSocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

In a trial examining whether cognitive-behavioral therapy (CBT) could be improved by integrating motivational interviewing (MI) to target resistance, MI-CBT outperformed CBT over 12-month follow-up (Westra, Constantino, & Antony, 2016). Given that effectively addressing resistance is both a theoretically and an empirically supported mechanism of MI's additive effect, we explored qualitatively patients' experience of resistance, possibly as a function of treatment. For 5 patients from each treatment who exhibited early in-session change ambivalence, and thus were at risk for later resistance, we conducted interpersonal process recall interviews after a session. Transcripts were analyzed with grounded theory and consensual qualitative research. A salient contrast in patient narratives was a sense of compliance engendered in standard CBT versus connection in MI-CBT. Yet both narratives supported the superordinate category of resistance as an interpersonal process triggered by patient perceptions of therapist beliefs and behaviors. Findings contribute to the conceptualization of resistance from patients' first-hand accounts.

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 score0.091
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.287
GPT teacher head0.620
Teacher spread0.333 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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