Evaluating the Psychological Impact of Forest Bathing: A Meta-Analysis of Emotional State Outcomes
Bibliographic record
Abstract
Background: Forest bathing, a therapeutic practice involving immersion in natural forest environments, has gained attention for its potential mental health benefits. This meta-analysis evaluates the impact of forest bathing on psychological parameters such as tension-anxiety, depression, anger-hostility, fatigue, confusion, and vigor. Methods: A meta-analysis was conducted on studies assessing forest bathing's effects on psychological states. Six studies were included, analyzing data using fixed and random effects models. Results: The analysis of six studies with 296 participants revealed a strong positive correlation between forest bathing and reduced tension-anxiety, with correlation coefficients of 0.634 (fixed effects) and 0.613 (random effects). Both models were statistically significant (p < 0.001), despite moderate to high heterogeneity (I² = 67.57%). For depression, five studies (277 participants) showed a significant reduction, with a stronger correlation in the random effects model (0.557) compared to the fixed effects model (0.432). Anger-hostility was similarly reduced, with high heterogeneity (I² = 90.12%) and correlation coefficients of 0.741 (fixed) and 0.767 (random). Fatigue, assessed in six studies (296 participants), also showed significant reductions, with moderate heterogeneity (I² = 45.16%). Confusion was moderately reduced (I² = 29.52%), with correlation coefficients of 0.339 (fixed) and 0.323 (random). Lastly, vigor showed a weak positive association, with a correlation coefficient of 0.269. Conclusion: The findings confirm the therapeutic potential of forest environments in promoting mental health. Given the observed positive effects, forest bathing could be integrated into public health strategies as a non-pharmacological intervention for stress and mood disorders.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".