A Systematic Review and Meta-Analysis of Nature Walk as an Intervention for Anxiety and Depression
Bibliographic record
Abstract
Scientific research has widely examined the therapeutic and health benefits of being in contact with natural environments. Nature walk have been proposed as a cost-effective and inclusive method for successfully exploiting nature for the promotion of health and well-being. Depression and anxiety symptoms have been shown to benefit from nature walk. Despite recent empirical findings published in the scientific literature, a summary quantitative work on the effect of nature walk on depression and anxiety does not yet exist. The present systematic review and meta-analysis quantitatively analyze and qualitatively discuss the studies published on the effect of nature walk on depression and anxiety published during the past decade. A database search as well as snowballing methods were used to retrieve eligible articles. The research question and literature search were based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Based on screening and retrieval processes, seven studies met the eligibility criteria and were then included in the quantitative meta-analysis. Risk of bias (RoB) analysis was used to evaluate the quality of the included studies using the Newcastle-Ottawa Scale. After a qualitative evaluation of the studies, data from six experiments were included in the meta-analysis. The meta-analysis show that nature walk effectively improve mental health. The findings were confirmed for the experiments reporting the quantitative data within groups (pre- and post-test) and between groups (experimental vs. control group).
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".