Helping Amid the Pandemic: Daily Affective and Social Implications of COVID-19-Related Prosocial Activities
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The novel coronavirus disease 2019 (COVID-19) pandemic may have prompted more engagement in prosocial activities, such as volunteering and support transactions. The day-to-day affective and social implications of these activities for adults of different ages are unknown. The current study examined associations of daily prosocial activities with affective and social well-being, and whether these associations varied by age. RESEARCH DESIGN AND METHODS: Participants ages 18-91 in Canada and the United States (N = 1,028) completed surveys for 7 consecutive evenings about their daily experiences of COVID-19-related prosocial activities (formal volunteering, support provision, support receipt), positive and negative affect, and satisfaction with social activities and relationships. Analyses were conducted using multilevel modeling and accounted for a range of potential confounding factors (e.g., sociodemographics, work, family, caregiving, daily stressors). RESULTS: Older age predicted more frequent formal volunteering, as well as more support provision and support receipt due to COVID-19. In particular, middle-aged and older adults provided more emotional support than younger adults, middle-aged adults provided the most tangible support, and older adults received the most emotional support. All three types of prosocial activities were associated with higher positive affect and greater social satisfaction on days when they occurred. Providing COVID-19-related support further predicted lower same-day negative affect. Age did not significantly moderate these associations. DISCUSSION AND IMPLICATIONS: Older age was related to more frequent engagement in prosocial activities during the COVID-19 crisis. These activities were associated with improved daily affective and social well-being for adults of all ages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".