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Record W2770297821 · doi:10.1002/jclp.22551

Motivation and treatment credibility predict alliance in cognitive behavioral treatment for youth with anxiety disorders in community clinics

2017· article· en· W2770297821 on OpenAlexaff
Krister W. Fjermestad, Matthew D. Lerner, Bryce D. McLeod, Gro Janne Wergeland, Bente Storm Mowatt Haugland, Odd E. Havik, Lars‐Göran Öst, Wendy K. Silverman

Bibliographic record

VenueJournal of Clinical Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsAllianceCredibilityPsychologyAnxietySession (web analytics)Clinical psychologyPsychotherapistCognitive restructuringPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined whether motivation and treatment credibility predicted alliance in a 10-session cognitive behavioral treatment delivered in community clinics for youth anxiety disorders. METHOD: = 11.4 years, standard deviation = 2.1, range 8-15 years, 49.5% boys) with anxiety disorders-rated treatment motivation at pretreatment and perceived treatment credibility after session 1. Youths and therapists (YT) rated alliance after session 3 (early) and session 7 (late). Hierarchical linear models were applied to examine whether motivation and treatment credibility predicted YT early alliance, YT alliance change, and YT alliance agreement. RESULTS: Motivation predicted high early YT alliance, but not YT alliance change or alliance agreement. Youth-rated treatment credibility predicted high early youth alliance and high YT positive alliance change, but not early therapist alliance or alliance agreement. CONCLUSION: Conclusion Efforts to enhance youth motivation and treatment credibility early in treatment could facilitate the formation of a strong YT alliance.

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 score0.183
Threshold uncertainty score0.572

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 science0.0000.000
Research integrity0.0000.001
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 head0.312
GPT teacher head0.515
Teacher spread0.203 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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