The Impacts of Forest Therapy on the Physical and Mental Health of College Students: A Review
Bibliographic record
Abstract
The aim of this review is to investigate the impacts of various forest therapy activities on the physical and mental health of college students. Additionally, it evaluates the research methodologies and existing issues in current studies, providing an important agenda for future research. Research was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The findings suggest significant effects of forest therapy activities on the physiology and psychology of college students, notably improving the cardiovascular system, enhancing the immune system, boosting emotional well-being, alleviating job-related stress, and enhancing academic performance. This study further clarifies forest therapy as an emerging and effective intervention to reduce stress levels among college students, particularly when carried out continuously in easily accessible campus forest environments. Such therapeutic activities could serve as a component of daily stress-relieving programs for college students. This assessment offers valuable information for college students, educational institutions, and policymakers to promote the development of forest therapy on university campuses. However, some of the studies included in this investigation lacked methodological rigor. Future research should employ rigorous study designs to assess the long-term impacts of various forest therapy approaches on the mental and physical health of college students and identify the primary influencing factors. This will aid in determining suitable content, forms, and strategies for forest therapy projects tailored to college students, thereby maximizing the potential benefits of forest therapy on their mental and physical well-being.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".