Easing anxiety symptoms through leisure activities during social isolation: Findings from nationally representative samples
Bibliographic record
Abstract
Public health interventions implemented during the COVID-19 pandemic may exacerbate anxiety symptoms for many. We conducted this study to better understand the role of leisure activity in promoting mental wellness during times of social isolation and reduced access to recreation facilities and mental health support services. We analyzed nationally representative survey data collected by Statistics Canada as part of the Canadian Perspectives Survey Series (CPSS) during May 4-10 (CPSS 2) and July 20 to 26, 2020 (CPSS 4). Data related to leisure activity and anxiety symptoms as measured by a score of more than 10 on the General Anxiety Disorder scale were examined using descriptive and log-binomial regression analyses. Survey sampling weights were applied in all analyses, and regression results were adjusted for sociodemographic characteristics. Exercise and communication with friends and loved ones were the most frequently reported leisure activity. Prevalence of moderate to severe anxiety symptoms reported by participants was lower in CPSS 4 compared to CPSS 2. Results of adjusted log-binomial regression analyses revealed lower prevalence of moderate to severe anxiety symptoms in those who engaged in exercise and communication, while those who meditated exhibited higher prevalence. In conclusion, leisure activities, such as exercise and communication with loved ones, can promote mental wellness. Future research should clarify the role of meditation for mental wellness promotion during periods of social isolation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".