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Record W4416958423 · doi:10.1016/j.invent.2025.100894

Adaptive actions as a mechanism of change in transdiagnostic internet-delivered cognitive behavioral therapy: Comparison with homework engagement

2025· article· en· W4416958423 on OpenAlexafffund
Heather D. Hadjistavropoulos, Blake F. Dear, Nickolai Titov, Ram P. Sapkota

Bibliographic record

VenueInternet Interventions · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Regina
FundersCanadian Institutes of Health ResearchMinistry of Health, SaskatchewanGovernment of Western Australia
KeywordsMechanism (biology)ReciprocalAnxietyCognitionAdaptive behaviorBehavioral inhibition

Abstract

fetched live from OpenAlex

= 625) received a transdiagnostic ICBT program in routine care. Outcomes included depression, generalized anxiety, posttraumatic stress, panic, social anxiety, insomnia, functional impairment, and wellbeing. Adaptive actions were assessed with the Things You Do Questionnaire-15 Item (TYDQ-15) and homework engagement with the Homework Reflection Questionnaire (HWRQ). TYDQ-15 scores improved from pre- to post-treatment and were maintained at follow-up. Although correlated, mid-treatment TYDQ-15 scores more consistently predicted outcomes than homework engagement and partially mediated changes across all assessed outcomes. Reciprocal analyses revealed bidirectional relationships between adaptive actions and anxiety and wellbeing, and partially bidirectional associations with depression. Overall, mid-treatment adaptive actions emerged as a stronger and more consistent mechanism of change than homework engagement across diverse outcomes. Findings underscore the importance of research on the benefits of encouraging adaptive actions early in treatment.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.264
GPT teacher head0.481
Teacher spread0.216 · 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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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