MétaCan
Menu
Back to cohort

Preliminary Findings for Group Transdiagnostic Behavior Therapy for Affective Disorders Among Youths

2020· article· en· W3081050908 on OpenAlexaffabout
Daniel F. Grös, Colleen Merrifield, Jennifer Hewitt, Ashleigh Elcock, Karen Rowa, Randi E. McCabe

Bibliographic record

VenueAmerican Journal of Psychotherapy · 2020
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsAnxietyClinical psychologyPsychologyDepression (economics)Cognitive behavioral therapyCognitionGroup psychotherapyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The literature on transdiagnostic psychotherapy among youths is limited. Group transdiagnostic behavior therapy (TBT) has been shown to be effective for adults with affective disorders and may contain beneficial features for youths (e.g., behavioral focus, group format, ease of dissemination, and diversity of targeted diagnoses). This study aimed to investigate group TBT among youths in Canada to determine its feasibility and efficacy. METHODS: Twenty participants (ages 16-19) diagnosed as having a principal anxiety disorder completed 12 sessions of group TBT. Symptoms of anxiety, depression, and transdiagnostic impairment were assessed pre- and posttreatment. RESULTS: Participants demonstrated significant improvements on measures of anxiety (general, cognitive, and somatic) and stress, with moderate effect sizes. Findings for symptoms of depression and transdiagnostic impairment were unreliable, with small effect sizes. CONCLUSIONS: These findings provide preliminary support for the use of group TBT among youths with anxiety disorders. Future research should incorporate comparison groups and larger samples.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.327
Teacher spread0.306 · 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 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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of PsychotherapySame topicPersonality Disorders and PsychopathologyFrench-language works237,207