A Web-Based Group Cognitive Behavioral Therapy Intervention for Symptoms of Anxiety and Depression Among University Students: Open-Label, Pragmatic Trial
Bibliographic record
Abstract
Background Anxiety and depression are common among university students, and university counseling centers are under pressure to develop effective, novel, and sustainable interventions that engage and retain students. Group interventions delivered via the internet could be a novel and effective way to promote student mental health. Objective We conducted a pragmatic open trial to investigate the uptake, retention, treatment response, and level of satisfaction with a remote group cognitive behavioral therapy intervention designed to reduce symptoms of anxiety and depression delivered on the web to university students during the COVID-19 pandemic. Methods Preintervention and postintervention self-reported data on anxiety and depression were collected using the Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9. Satisfaction was assessed postintervention using the Client Satisfaction with Treatment Questionnaire. Results A total of 175 students were enrolled, 158 (90.3%) of whom initiated treatment. Among those initiating treatment, 86.1% (135/158) identified as female, and the mean age was 22.4 (SD 4.9) years. The mean number of sessions attended was 6.4 (SD 2.8) out of 10. Among participants with clinically significant symptoms at baseline, mean symptom scores decreased significantly for anxiety (t56=11.6; P<.001), depression (t61=7.8; P<.001), and composite anxiety and depression (t60=10.7; P<.001), with large effect sizes (d=1-1.5). Remission rates among participants with clinically significant baseline symptoms were 67.7%-78.9% and were not associated with baseline symptom severity. High overall levels of satisfaction with treatment were reported. Conclusions The results of this study serve as a proof of concept for the use of web-based group cognitive behavioral therapy to promote the mental health of university students.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".