Teaching and Practicing Cognitive-Behavioral and Mindfulness Skills in a Web-Based Platform among Older Adults through the COVID-19 Pandemic: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
The outbreak of the COVID-19 pandemic has led to an acceleration in the development of web-based interventions to alleviate related mental health impacts. The current study explored the effects of a short-term digital group intervention aimed at providing cognitive behavioral and mindfulness tools and skills to reduce loneliness and depression and to increase social support among older adults in Israel. This pilot randomized controlled trial included community-dwelling older adults (n = 82; aged between 65–90 years; 80% female) who were randomized either to an intervention group (n = 64) or a wait-list control group (n = 18). The intervention included seven online sessions, over 3.5 weeks. Depression, loneliness, and social support measures were administered at baseline, immediately post-intervention, and at 1-month follow-up. Repeated measures ANOVA revealed statistically and clinically significant reductions in depression in the intervention group, with results maintained at one-month follow-up. Loneliness levels also significantly decreased post-intervention; however, this benefit was not maintained at one-month follow-up. Social support slightly increased both post-intervention and 1-month follow-up—but these changes were not statistically significant. There were no overall changes for the wait-list control group. Our intervention provided promising evidence regarding the effectiveness of an online group intervention to alleviate mental health effects and to promote the coping of older adults during the COVID-19 pandemic. This relatively simple model can be effectively utilized by communities globally to help connect lonely and isolated older inhabitants, both during the pandemic and in more routine times.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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) | 0.007 | 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".