Does forest immersion benefit everyone? Investigating baseline stress and scene type as thresholds for neuropsychological restoration
Bibliographic record
Abstract
This study investigates the neuropsychological effects of forest immersion on individuals with varying baseline stress levels. A total of 120 participants from Nanjing, China, were categorized into low, moderate, and high-stress groups based on the Perceived Stress Scale (PSS). Participants engaged in seated and guided walking experiences in three distinct forest environments: dense forest, grassland-water, and woodland edge. Pre- and post-immersion assessments included EEG recordings and self-reported measures of emotion, anxiety, nature connectedness, and rumination. Forest immersion significantly improved psychological well-being, with the low-stress group showing the greatest improvements in positive affect (ΔPANAS-PA: +4.2 ± 4.9) and state anxiety (ΔSTAI: -5.5 ± 6.3). The moderate-stress group showed similar trends but with smaller effect sizes, while the high-stress group exhibited more heterogeneous responses, with some individuals showing no improvement or even negative changes, consistent with the "wellness counter-effect." The dense forest environment produced the most significant benefits for the low- and moderate-stress groups, while high-stress individuals showed more complex neural and psychological responses, suggesting that high baseline stress may attenuate the restorative benefits of forest exposure. The findings suggest that the restorative effects of forest immersion are not universally applicable but are influenced by baseline stress levels and environmental characteristics. This study underscores the importance of personalized, context-sensitive interventions and provides valuable insights for the design of nature-based wellness programs, emphasizing the need to consider individual psychological traits and stress levels to optimize therapeutic outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".