The Impact of a Structured Social Workshop on Risk Behaviors and Mood Regulation
Bibliographic record
Abstract
This study aimed to evaluate the effectiveness of a Social Connectedness Workshop in mitigating risk-taking behaviors and improving mood regulation among adults experiencing mild to moderate levels of social disconnection. It hypothesized that increased social connectedness through structured intervention would lead to significant improvements in these psychological domains. A randomized controlled trial was conducted with 30 participants, aged 18-45, who were assigned to either an 8-session Social Connectedness Workshop (experimental group) or a control group receiving no intervention. Assessments using the Balloon Analogue Risk Task (BART) for risk-taking behaviors and the Difficulties in Emotion Regulation Scale (DERS) for mood regulation were conducted at baseline, post-intervention, and at a three-month follow-up. Data analysis was performed with SPSS-27, employing Analysis of Variance with repeated measurements and Bonferroni post-hoc tests. Participants in the experimental group showed significant reductions in risk-taking behaviors, with mean scores decreasing from 43.99 (SD = 5.44) at baseline to 37.39 (SD = 6.99) at follow-up (p < 0.01). Mood regulation also improved significantly, with mean scores increasing from 82.99 (SD = 13.77) to 90.49 (SD = 13.55) in the same group (p < 0.01). No significant changes were observed in the control group across both variables. The intervention group's results displayed significant time, group, and interaction effects, indicating the workshop's effectiveness in achieving the study's objectives. The Social Connectedness Workshop effectively reduced risk-taking behaviors and enhanced mood regulation among participants, underscoring the importance of social ties and emotional support in psychological well-being. These findings suggest that structured social connectedness interventions can be a valuable component of mental health strategies aimed at reducing risk-taking behaviors and improving mood regulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".