Satisfaction, engagement, and outcomes in internet-delivered cognitive behaviour therapy adapted for people of diverse ethnocultural groups: an observational trial with benchmarking
Bibliographic record
Abstract
Introduction: Depression and anxiety are the most common mental health disorders worldwide. Internet-Delivered Cognitive Behaviour Therapy (ICBT) can reduce barriers to care to broad cross sections of the population. However, People of Diverse Ethnocultural Backgrounds (PDEGs) other than White/Caucasian underutilize mental health services and are under represented in clinical trials of psychological interventions. Methods: To address this research gap we adapted an evidence-based ICBT program for PDEGs. The current pilot study explores the engagement, satisfaction, and effectiveness in the adapted ICBT program by PDEGs (N=41) when benchmarked against a sample of PDEGs (N=134) who previously completed a non-adapted version of the ICBT program. Results: = 1.24, 95% CI [0.69, 1.79] were found for depression and anxiety, respectively. Further, 81.8% of the PDEGs who received the adapted ICBT reported overall satisfaction, 90.9% reported increased confidence in managing symptoms, and 70.7% completed majority of the psychoeducational lessons in the ICBT program. Conclusion: No statistically significant differences in the clinical outcomes, engagement, and satisfaction were found between the pilot study and benchmark sample. Future directions for ICBT research with PDEGs are described. Clinical trial registration: https://beta.clinicaltrials.gov/study/NCT05523492, identifier NCT05523492.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".