Bibliographic record
Abstract
The World Health Organization (WHO) defines health as not simply the absence of disease or infirmity, but a state in which a complete sense of well-being is present (World Health Organization, 2003). A healthy population must, therefore, consist of individuals that display positive attributes associated with well-being and, in circumstances when well-being has diminished, have access to resources that increase it. Evidence has indicated that outdoor recreation in forest environments can help improve well-being (Hartig, Mang, & Evans, 1991; Horiuchi et al., 2013; Lee et al., 2011), but these studies have often relied on large natural parks as the experimental context or setting, and participants are often given prolonged exposure (hours or days) (Hartig et al., 1991; Morita et al., 2007). The goal of this research is to examine the impact of different accessible urban forest environments on the well-being of university students. It focuses on feelings of happiness, vitality, mood and stress reduction, while investigating the link between well-being and nature relatedness. Key measures include the Vitality Scale, the Positive Affect Negative Affect Schedule (PANAS) Scale, Overall Happiness Scale, Profile of Mood (POM) Scale, Nature Relatedness scale, heart rate, and blood pressure. The study uses a pre-test- post-test design and one-way repeated measures ANOVA to analyze results. Results demonstrated that the urban forest environment was associated with elevated levels of happiness, vitality, mood, and decreased heart rate. Minimal differences were found for blood pressure. Similarly, exposure to the forest stream environment showed no significant differences between any of the well-being indicators and the forest environment. The overall results indicated that small accessible urban forest environments can improve well-being, providing support for the biophilia hypothesis, the concept of nature relatedness, and the value of outdoor recreation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".