Exploring the influence of loneliness and social isolation in transdiagnostic internet-delivered cognitive behavioral therapy for depression and anxiety.
Bibliographic record
Abstract
OBJECTIVE: Past research shows that social factors play an important role in mental health outcomes, but there is limited research on how these factors influence Internet-delivered cognitive behavior therapy (ICBT). This study investigated the prevalence of subjective (i.e., feelings of loneliness) and objective social isolation among patients receiving transdiagnostic ICBT. We explored whether social factors change over treatment and moderate treatment effectiveness and engagement. METHOD: This study used data collected in a routine ICBT clinic. Among clients with elevated depression and/or anxiety who started ICBT (n = 625), we analyzed measures of depression, anxiety, loneliness, and social engagement administered at pretreatment, various points during the treatment and 20 weeks follow-up. RESULTS: Pretreatment prevalence of frequent loneliness and social isolation was 75.7% and 54.6%, respectively. Depression and anxiety decreased over time, with large effect sizes from pretreatment to follow-up (depression d = 1.56; anxiety d = 1.63). Loneliness decreased significantly (d = 0.69), while social engagement improved moderately (d = 0.37). Higher pretreatment loneliness was associated with higher average levels of depression across the treatment period and with fewer completed treatment lessons. CONCLUSIONS: Overall, results indicate that loneliness and social isolation are prevalent among clients seeking ICBT and both decrease during transdiagnostic ICBT. More frequent loneliness during the treatment was associated with lower engagement and smaller treatment gains, while social isolation was not. These findings suggest that further research is warranted on how to address loneliness within ICBT. It also suggests that social isolation can be reduced by means of transdiagnostic ICBT. (PsycInfo Database Record (c) 2025 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".