‘When you give kindness out, you get it back ten times more’: Ontario adults’ prosocial behaviour during the first 16 months of the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has provided a collective opportunity to engage in prosocial behaviours, including kindness; however, little is known about the long-term impacts of the pandemic on such behaviours. As a part of a larger study (Health Outcomes for Adults During and Following the COVID-19 Pandemic), the purpose of this mixed methods research was two-fold: (1) to quantitatively explore adults' prosocial behaviour over time during the first 16 months of the pandemic in Ontario, Canada (April 2020-August 2021); and, (2) to more deeply explore, via focus groups, a sub-sample of Ontario adults' lived experiences of prosocial behaviour (assessed March 2022). A total of 2,188 participants were included in this study, with the majority of participants identifying as female (89.5%). At three time points, participants completed online questionnaires which included demographics, Prosocialness Scale for Adults, and three global kindness questions. A subset of participants (n = 42) also participated in one of six focus groups exploring their experiences of prosocial behaviour during the pandemic. A series of one-way repeated measures ANOVAs revealed that participants' self-reported prosocial behaviour increased significantly over time, while participants' awareness of kindness, engagement in acts of kindness, and view of kindness as crucial significantly decreased. Thematic analysis revealed three main themes: (1) shift in prosocial behaviour during the pandemic; (2) kindness from various perspectives; and, (3) prosocial burnout. This study provides insight into the longer-term effects of the pandemic on adults' prosocial behaviours and should be leveraged to help understand how individuals respond in times of crises.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".