Heterogeneity of Effects in a Prosociality‐Based Intervention to Reduce Loneliness and Increase Social Contact
Bibliographic record
Abstract
OBJECTIVE: This study evaluates the effects of an act of kindness intervention on increasing daily social contact and reducing loneliness among community adults experiencing loneliness. It also explores heterogeneity in effects and potential moderators, including individual differences in baseline social health and intervention implementation. METHOD: In a randomized controlled trial, 208 adults were randomly assigned to perform daily acts of kindness for others (Kindness condition) or take a short break for themselves (Control condition) for 2 weeks. Dairy assessments of loneliness and social contact were collected 3 days before and after the intervention. RESULTS: We did not find consistent average effects. Although loneliness decreased in the Kindness condition, the reduction was not significantly greater than in the Control condition. In contrast, a group difference was observed in social contact, which remained stable in the Kindness condition but declined in the Control condition. Notably, significant individual differences emerged: the intervention was more effective for participants with higher baseline social anxiety and loneliness, and when a greater proportion of prosocial acts targeted weak social ties and a smaller proportion targeted strangers. CONCLUSIONS: These findings highlight the importance of identifying individual differences-for whom and under what conditions prosociality-based interventions are most effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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 | 0.001 | 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".