How leisure involvement impacts visitors’ perceived health benefits in urban forest parks: examining the moderating role of place attachment
Bibliographic record
Abstract
Urban forest park leisure is a significant means for residents to achieve health and well-being, thus possessing high public health value. However, the relationship between visitors' leisure involvement and perceived health benefits has not been clarified. This study introduced restorative experiences and positive emotions as mediators and place attachment as a psychological moderator, innovatively constructing a stimulus-organism-response-moderator (SORM) integrated model. This moderated mediation model aimed to examine the mechanism through which visitors' leisure involvement influenced perceived health benefits. A field survey was conducted in Fuzhou National Forest Park in Fujian, China, resulting in the collection of 588 valid questionnaires. The results showed that visitors' leisure involvement positively impacted restorative experiences and positive emotions. Restorative experiences and positive emotions completely mediated the indirect relationship between visitors' leisure involvement and perceived health benefits. Place attachment enhanced the impact of restorative experiences on perceived health benefits, thereby positively moderating the mediation effect of restorative experiences. Place attachment also diminished the impact of positive emotions on perceived health benefits, thereby negatively moderating the mediation effect of positive emotions. Therefore, significant differences existed in the psychological processes involved in acquiring perceived health benefits among visitors with different levels of place attachment. Our findings might enrich the existing knowledge of place attachment and forest health benefits, providing valuable references for designing and optimizing urban forest parks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".