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Record W2016161365 · doi:10.1080/10503301003657395

Therapy was not what I expected: A preliminary qualitative analysis of concordance between client expectations and experience of cognitive–behavioural therapy

2010· article· en· W2016161365 on OpenAlexafffund
Henny A. Westra, Adi Aviram, Marissa E. Barnes, Lynne Angus

Bibliographic record

VenuePsychotherapy Research · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute of Mental HealthYork University
KeywordsOutcome (game theory)PsychologySurpriseExpectancy theoryConcordancePsychotherapistCognitionQualitative researchCognitive therapyClinical psychologyPsychiatrySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Expectancy violations have generally been neglected in psychotherapy research but may have important implications for therapy process and outcome. A qualitative approach was used to examine discrepancies between actual experience and expectations in client posttreatment accounts of cognitive-behavioural therapy. Nine good- and nine poor-outcome cases were included. Good-outcome clients frequently reported disconfirmation of process expectations, including surprise that therapy was collaborative, that they had the freedom to direct therapy, and that they were comfortable and could trust the process. Poor-outcome clients generally failed to report such experiences. Good-outcome clients also reported gaining more from treatment than expected, whereas poor-outcome clients reported being disappointed. These findings suggest an important role for expectancy disconfirmation in 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 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)0.017
metaresearch head score (Gemma)0.044
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 score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.044
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.008
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.224
GPT teacher head0.546
Teacher spread0.322 · 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

Citations61
Published2010
Admission routes2
Has abstractyes

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