Internet-Delivered Cognitive Behavioral Therapy for Postsecondary Students: Randomized Factorial Trial for Examining Motivational Interviewing and Booster Lessons
Bibliographic record
Abstract
BACKGROUND: Internet-delivered cognitive behavioral therapy (ICBT) can improve access to mental health care for students, although high attrition rates are concerning and little is known about long-term outcomes. Motivational interviewing (MI) exercises and booster lessons can improve engagement and outcomes in face-to-face cognitive behavioral therapy. OBJECTIVE: This study aimed to examine the use of pretreatment MI exercises and booster lessons in ICBT for postsecondary students. METHODS: In this factorial trial (factor 1: web-based MI before treatment; factor 2: self-guided booster lesson 1 month after treatment), 308 clients were randomized to 1 of 4 treatment conditions, with 277 (89.9%) clients starting treatment. All clients received a 5-week transdiagnostic ICBT course (the UniWellbeing course). Primary outcomes included changes in depression, anxiety, and perceived academic functioning from before treatment to after treatment and at the 1-month and 3-month follow-ups. RESULTS: Overall, 54% (150/277) of students completed treatment and reported large improvements in symptoms of depression and anxiety and small improvements in academic functioning after treatment, which were maintained at the 1-month and 3-month follow-ups. Pretreatment MI did not contribute to better treatment completion or engagement, although small between-group effects favored MI for reductions in depression (Cohen d=0.23) and anxiety (Cohen d=0.25) after treatment. Only 30.9% (43/139) of students randomized to one of the booster conditions accessed the booster. Overall, no main effects were found for the booster. Subanalyses revealed that clients who accessed the booster had larger decreases in depressive symptoms (Cohen d=0.31) at the 3-month follow-up. No interactions were found between MI and the booster. CONCLUSIONS: Rather than offering MI before treatment, clients may experience more benefits from MI exercises later in ICBT when motivation wanes. The low uptake of the self-guided booster limited our conclusions regarding its effectiveness. Future research should examine offering a booster for a longer duration after treatment, with therapist support and a longer follow-up period. TRIAL REGISTRATION: ClinicalTrials.gov NCT04264585; https://clinicaltrials.gov/ct2/show/NCT04264585.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".