Understanding health and well-being outcomes associated with protected coastal ecosystems: A Fundy National Park case study.
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted the mental health and well-being of Canadians, prompting many to turn to nature for consolation and social interaction. However, there is a lack of evidence linking these outcomes specifically to coastal areas. To address this gap, we conducted a self-reported survey at Fundy National Park, New Brunswick, Canada, in summer 2022. Over 400 visitors participated, revealing nature enjoyment, physical health, and mental well-being as primary motivators for park visitation. Notably, mental well-being showed significant differences across gender, sexual identity, and ethnicity, with males, straight individuals, and others exhibiting distinct preferences compared to females, 2SLGBTQIA+ individuals, and BIPOC participants. Additionally, visitors reported substantial health benefits from coastal visits, particularly linked to place attachment. When examining the level of attachment associated with ethnicity, statistical differences emerged, indicating a need for further investigation into how cultural backgrounds influence individuals’ connections to coastal environments. Recommendations for visitor management, including inclusive strategies such as educational programs and interpretation services, are discussed. Future research opportunities in protected areas should focus on exploring the health benefits of coastal areas across Canada, investigating inclusive access to nature, and employing various methodological approaches to enhance our understanding of the intricate relationship between individuals and coastal areas. Future research should further explore coastal benefits and the perspectives of marginalized visitors, potentially through longitudinal studies and follow-up surveys, to ensure inclusive access to coastal ecosystems and the health and well-being benefits they provide.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".