Prosocial behavior promotes positive emotion during the COVID-19 pandemic.
Bibliographic record
Abstract
= 1,623) conducted during the early stage of pandemic (April 2020), we examined this question by randomly assigning participants to engage in other- or self-beneficial action. For the first time, we manipulated whether prosocial behavior was related to the source of stress (coronavirus disease 2019 [COVID-19]): Participants purchased COVID-19-related (personal protective equipment, PPE) or COVID-19-unrelated items (food/writing supplies) for themselves or someone else. Consistent with preregistered hypotheses, prosocial (vs. non-pro-social or proself) behavior led to higher levels of self-reported positive affect, empathy, and social connectedness. Notably, we also found that psychological benefits were larger when generous acts were unrelated to COVID-19 (vs. related to COVID-19). When prosocial and proself spending involved identical COVID-19 PPEs items, prosocial behavior's benefits were detectable only on empathy and social connectedness, but not on posttask positive affect. These findings suggest that while there are boundary conditions to be considered, generous action offers one strategy to bolster well-being during the pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".