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Record W2171303252 · doi:10.1016/j.janxdis.2014.09.018

Therapist-assisted Internet-delivered cognitive behavior therapy for depression and anxiety: Translating evidence into clinical practice

2014· article· en· W2171303252 on OpenAlexafffund
Heather D. Hadjistavropoulos, Nicole E. Pugh, Marcie Nugent, Hugo Hesser, Gerhard Andersson, Martin Ivanov, Catherine Butz, Gregory P. Marchildon, Gordon J. G. Asmundson, Britt Klein, David Austin

Bibliographic record

VenueJournal of Anxiety Disorders · 2014
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Regina
FundersCanadian Institutes of Health ResearchSaskatchewan Health Research Foundation
KeywordsAnxietyPsychologyDepression (economics)CognitionPanic disorderPanicClinical psychologyClinical PracticePsychotherapistQuality of life (healthcare)PsychiatryPhysical therapyMedicine

Abstract

fetched live from OpenAlex

This dissemination study examined the effectiveness of therapist-assisted Internet-delivered Cognitive Behavior Therapy (ICBT) when offered in clinical practice. A centralized unit screened and coordinated ICBT delivered by newly trained therapists working in six geographically dispersed clinical settings. Using an open trial design, 221 patients were offered 12 modules of ICBT for symptoms of generalized anxiety (n=112), depression (n=83), or panic (n=26). At baseline, midpoint and post-treatment, patients completed self-report measures. On average, patients completed 8 of 12 modules. Latent growth curve modeling identified significant reductions in depression, anxiety, stress and impairment (d=.65-.78), and improvements in quality of life (d=.48-.66). Improvements in primary symptoms were large (d=.91-1.25). Overall, therapist-assisted ICBT was effective when coordinated across settings in clinical practice, but further attention should be given to strategies to improve completion of treatment modules.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.068
GPT teacher head0.458
Teacher spread0.390 · 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 designOther design
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

Citations63
Published2014
Admission routes2
Has abstractyes

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