Engaging with Natural Beauty May Be Related to Well-Being Because It Connects People to Nature: Evidence from Three Cultures
Bibliographic record
Abstract
Connecting with nature has been described by some as an important psychological need. Indeed, research shows that a strong connection to nature predicts flourishing across a wide range of well-being indices. Engaging with natural beauty may be one route by which people satisfy this presumed need to connect with nature. Based on this reasoning, the purpose of the current research was to investigate whether nature connectedness mediates the relationship between engagement with natural beauty (i.e., the tendency to notice and be moved by beauty in nature) and well-being in three different cultures. Four cross-sectional surveys involving Canadian, Japanese, and Russian undergraduate students were conducted (N = 1,390). Engagement with natural beauty and nature connectedness were positively associated with a variety of well-being measures. Moreover, we found relatively consistent support for the indirect effect of nature connectedness in explaining the relationship between engagement with natural beauty and well-being. This finding replicated across five different measures and indices of well-being, two different measures of nature connectedness, and three different cultures. Overall, this research suggests that engaging with natural beauty may have an impact on well-being by promoting a stronger subjective connection with nature. Key Words: Engagement with natural beauty—Nature connectedness—Well-being—Meaning—Biophilia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".