If it makes you happy: Engaging in kind acts increases positive affect in socially anxious individuals.
Bibliographic record
Abstract
Social anxiety is associated with low positive affect (PA), a factor that can significantly affect psychological well-being and adaptive functioning. Despite suggestions that individuals with high levels of social anxiety would benefit from PA enhancement, the feasibility of doing so remains an unanswered question. Accordingly, in the current study, individuals with high levels of social anxiety (N = 142) were randomly assigned to conditions designed to enhance PA (Kind Acts), reduce negative affect (NA; Behavioral Experiments), or a neutral control (Activity Monitoring). All participants engaged in the required activities for 4 weeks and completed prepost questionnaires measuring mood and social goals, as well as weekly email ratings of mood, anxiety, and social activities. Both the prepost and weekly mood ratings revealed that participants who engaged in kind acts displayed significant increases in PA that were sustained over the 4 weeks of the study. No significant changes in PA were observed in the other conditions. The increase in hedonic functioning was not due to differential compliance, frequency of social activities, or an indirect effect of NA reduction. In addition, participants who engaged in kind acts displayed an increase in relationship satisfaction and a decrease in social avoidance goals, whereas no significant changes in these variables were observed in the other conditions. This study is the first to demonstrate that positive affect can be increased in individuals with high levels of social anxiety and that PA enhancement strategies may result in wider social benefits. The role of PA in producing those benefits requires further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".