The Relationship Between Leisure Activities and Well-being During Social Isolation
Bibliographic record
Abstract
Leisure activities are effective ways to cope with stressful life events; however, more research is needed to understand its effects in social isolation. In the current study, we explored whether university students’ participation in leisure activities (such as baking, running, and yoga) has helped protect against some of the negative effects of social isolation due to the COVID-19 pandemic. Specifically, this correlational study aimed to discover (a) the relationship between leisure activity participation and psychological well-being and resilience during periods of social isolation; (b) the type of leisure activity (i.e., physical or nonphysical) that has the most positive effect on overall well-being; and (c) precisely why particular leisure activities were positively associated with well-being and resilience (e.g., do they increase social affiliation, self-efficacy, personal control, flow, and sense of meaning?). To answer these questions, 200 university students completed an online survey asking them about the frequency and type of their participation in leisure activities. Participant’s coronavirus anxiety, sense of well-being, and resiliency were the outcome variables. Results indicated a significant, positive association between leisure activity participation and overall well-being, demonstrated by a decrease in anxiety and an increase in well-being. Contrary to the literature, engagement in more non-physical leisure activities was associated with greater resiliency compared to physical activities. The mechanisms for the positive association were not clear. In sum, this study supports participation in leisure activities as a possible way to cope with social isolation and the negative effects of the current pandemic.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".