Does Spending Time in Nature Help Students Cope with the COVID-19 Pandemic?
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted our economy, social lives, and mental health, and it therefore provides a unique chance for researchers to examine how people cope with changes to their everyday activities. Research suggests that people may be spending more time in nature than they did pre-pandemic. The current study sheds light on how nature is being used to cope with the stresses of the global health crisis and lockdowns. Canadian undergraduate students (N = 559) filled out a questionnaire during the fall of 2020 about their pandemic experience, including their affects, life satisfaction, and feelings of flourishing and vitality, in addition to a wide variety of nature variables. The weekly exposures, the perceived increases or decreases in the exposure to nature during the pandemic, and the feelings of connectedness (nature relatedness) were assessed. Those who felt like they were spending more time in nature than they did pre-pandemic experienced more subjective well-being. Nature-related individuals were more likely to access nature and to appreciate it more during the pandemic than others, but all people (even those less connected) experienced well-being benefits from spending more time in nature. Going into nature appears to be an increasingly popular and effective coping strategy to boost or maintain subjective well-being during the 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".