Exploring the utility of transdiagnostic internet-delivered cognitive behavioral therapy for symptoms of adjustment disorder: A longitudinal observational study in a routine online care setting in Western Canada
Bibliographic record
Abstract
Abstract Background: Trials of disorder-specific Internet-delivered cognitive-behavioral therapy (ICBT) for Adjustment Disorder (AD) show moderate effect sizes but may have limited scalability in routine care settings, where clients present with a range of concerns. Transdiagnostic ICBT, which addresses common emotional and behavioral concerns irrespective of diagnosis, could address the need for effective and scalable treatments for symptoms of AD. Objective: This study aimed to evaluate the effectiveness of a transdiagnostic ICBT course for patients with high AD symptoms, and to investigate predictors of treatment outcomes, and treatment satisfaction. Methods: 793 participants received a therapist-guided, transdiagnostic ICBT course. The study measured changes in AD symptoms from pre-treatment to post-treatment to 3-month follow-up using the Adjustment Disorder – New Module 8 (ADNM-8). Results: The prevalence of high AD symptoms (defined as a score >23 on the ADNM-8) was 54.8% at pre-treatment. The study found large reductions in AD symptoms from pre-treatment to post-treatment (d = 1.29, 95% CI [1.13, 1. 45]) and from pre-treatment to the 3-month follow-up (d = 1.67, 95% CI [1.49, 1.85]). These effect sizes were comparable to those found in previous ICBT trials of AD-specific treatments. Approximately 70% of participants scored below clinical cut-off for high AD symptoms at post-treatment, and 79 % met this criterion at follow-up. Engagement in treatment and post-treatment satisfaction were similar between individuals with high AD symptoms and those without, with the majority (76.9%) completing four or more lessons and 81.7% reporting overall satisfaction. Conclusions: The findings suggest that transdiagnostic ICBT is an effective and acceptable treatment for AD symptoms, with results comparable to those of AD-specific interventions. The high prevalence of AD symptoms and stressful life events among participants in a routine care setting underscores the importance of early identification of individuals with high AD symptoms.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".