Does Social Distancing Affect the Stress Reduction and Attention Restoration of College Students in Different Natural Settings?
Bibliographic record
Abstract
The restorative benefits of the natural environment are crucial for human well-being and sustainable development. Although stress reduction and attention restoration through natural exposure have been quantified through physiological and psychological pathways, numerous studies have intentionally constructed idealized natural settings devoid of individuals to minimize interference. This deliberate approach has raised concerns about the accuracy of these restorative results, as real-world settings invariably involve other people. To address this issue, we designed and executed a randomized controlled experiment. By measuring physiological and psychological indicators and utilizing a two-way ANOVA and Tukey post-hoc comparison, we explored the restorative potential of natural settings within seven distinct social distances across five landscape types in virtual reality environments. The results revealed that beyond a social distance of 3.8 m, the presence of people had minimal impact on stress reduction, with attention restoration effects remaining consistently positive. Optimal physiological and psychological restoration is achieved when the social distance exceeded 20 m. Further exploration is warranted to elucidate the influence of landscape types on the restoration of natural environments. The findings provide valuable insights for the planning and design of restorative natural settings, supporting research endeavors aimed at improving human health and well-being and allowing for sustainable management.
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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) | 0.002 | 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".