Research on Companion-Based Forest Therapy and Its Physiological and Psychological Benefits to College Students
Bibliographic record
Abstract
With the growing pressures of modern society, physical and mental health issues have emerged as critical global concerns. Forest therapy (FT), a novel health management model that integrates natural environments with physical and mental healing, has gained increasing attention in recent years. However, mainstream FT approaches often overlook the psychological value of interpersonal interaction. Building upon traditional FT, this study proposes a new framework called companionship-based forest therapy (CBFT), which emphasizes the importance of emotional support within natural settings. CBFT is not intended as a replacement for conventional FT, but rather as an optimized approach that enhances its therapeutic effects by incorporating the element of companionship. This study aims to evaluate the physiological and psychological benefits of a novel intervention model-companion-based forest therapy (CBFT)-compared to conventional forest therapy models. Grounded in psychological theories and supported by empirical analysis, this study presents an applied framework of CBFT grounded in established psychological theories and validates its effectiveness through a comparative intervention involving 30 college students. Interpreted from the perspectives of ecological and humanistic psychology, the results indicate that CBFT significantly improves emotional regulation, reduces physiological stress responses, and enhances overall mental well-being. These findings highlight the value of social connection in FT practices and offer new directions for the development and application of forest therapy.
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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 | 0.003 |
| 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) | 0.004 | 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".