Engagement with homework in an Internet-delivered therapy predicts reduced anxiety and depression symptoms: A latent growth curve analysis.
Bibliographic record
Abstract
OBJECTIVE: Assigning homework to patients to facilitate skill development is an essential part of internet-delivered cognitive behavior therapy (ICBT). This study examined if patients' self-ratings of homework engagement, including conceptual (e.g., understandability, difficulty, perceived usefulness) and practical (e.g., effort put into reviewing the lessons, practicing skills, continuity in use of the learned skills) engagement predicts ICBT outcomes for anxiety and depression using a subsample of data from a previously published randomized controlled trial. METHOD: = 36.33) randomly assigned to complete Homework Reflection Questionnaires (HWRQ) were included in this study. Patients completed the Patient Health Questionnaire (PHQ-9) and generalized anxiety disorder (GAD-7) at pretreatment, midtreatment (4 weeks), posttreatment (8 weeks), and at follow-up (12 weeks). HWRQ related to each of five lessons were completed at the beginning of the subsequent lessons or at posttreatment (e.g., Lesson 1 HWRQ completed at start of Lesson 2). Latent growth curve modeling was used to test the effect of engagement with homework activities in reduction of anxiety and depression symptoms over time. RESULTS: Patient-rated homework engagement significantly predicted rate of change in depression and anxiety symptom severity but was not significantly associated with initial levels of either outcome. Patients who reported higher engagement with assigned homework activities achieved more symptom reduction over treatment and follow-up at 12 weeks. CONCLUSION: The findings provide evidence of the importance of patients' self-rated engagement with homework in ICBT as well psychometric evidence supporting use of homework ratings to assess engagement. Further studies are needed to replicate the association between homework engagement and reduction in anxiety and depression symptoms. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".