Using prosocial behavior to safeguard mental health and foster emotional well-being during the COVID-19 pandemic: A registered report of a randomized trial
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic, the accompanying lockdown measures, and their possible long-term effects have made mental health a pressing public health concern. Acts that focus on benefiting others-known as prosocial behaviors-offer one promising intervention that is both flexible and low cost. However, neither the range of emotional states prosocial acts impact nor the size of those effects is currently clear-both of which directly influence its attractiveness as a treatment option. OBJECTIVE: To assess the effect of prosocial activity on emotional well-being (happiness, belief that one's life is valuable) and mental health (anxiety, depression). METHODS: 1,234 respondents from the United States and Canada were recruited from Amazon's Mechanical Turk and randomly assigned (by computer software) to perform prosocial (N = 411), self-focused (N = 423), or neutral (N = 400) behaviors three times a week for three weeks. A follow-up assessment was given two weeks after the intervention. Participants were blind to alternative conditions. Analyses were based on 1052 participants (Nprosocial = 347, Nself = 365, Nneutral = 340). FINDINGS: Those in the prosocial condition did not differ on any outcome from those in the self-focused or neutral acts conditions during the intervention or at follow-up, nor did prosocial effects differ for those who had been negatively affected socially or economically by the pandemic (all p's > 0.05). Exploratory analyses that more tightly controlled for study compliance found that prosocial acts reduced anxiety relative to neutral acts control (β = -0.12 [95% CI: -0.22 to -0.02]) and increased the belief that one's life is valuable (β = 0.11 [95% CI: 0.03 to 0.19]). These effects persisted throughout the intervention and at follow-up. CONCLUSION: Prosocial acts may provide small, lasting benefits to emotional well-being and mental health. Future work should replicate these results using tighter, pre-registered controls on study compliance.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".