Health and Well-Being in Protected Natural Areas—Visitors’ Satisfaction in Three Different Protected Natural Area Categories in Catalonia, Spain
Bibliographic record
Abstract
Protected natural areas (PNAs) can be a source of health and well-being, but little research has been carried out regarding outcomes in terms of satisfaction (the difference between motivations and benefits). Inspired by previous research that examines the motivations and benefits perceived by visitors to various PNAs in Canada, and based on importance–performance analysis (IPA) and service quality gap (GAP) analysis theory, the aim of this study was to identify the outcomes generated by protected areas in terms of satisfaction, especially with regard to the PNAs’ individual protection categories. The study was based on survey data from visitors (n = 360) to three PNAs in Catalonia: one national park, one natural park and one periurban park. The results indicate that anticipated environmental, psychological, physical and social benefits were of major personal value in choosing to visit a PNA. The results indicate that, generally, visitors were satisfied with regard to the benefits anticipated. Differences between parks in this respect could be explained in part by sociodemographic factors and visitors’ behavior. The results are discussed in terms of their applicability and how they relate to the role of PNAs in the promotion of visitors’ health and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".