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Policy and Management Recommendations Informed by the Health Benefits of Visitor Experiences in Alberta’s Protected Areas

2016· article· en· W2319393762 on OpenAlexaffabout
Christopher J. Lemieux, Sean Doherty, Paul F.J. Eagles, Mark Groulx, Glen T. Hvenegaard, Joyce Gould, Elizabeth K. Nisbet, Francesc Romagosa

Bibliographic record

VenueJournal of Park and Recreation Administration · 2016
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsTrent UniversityUniversity of Northern British ColumbiaGovernment of AlbertaUniversity of AlbertaUniversity of WaterlooWilfrid Laurier University
Fundersnot available
KeywordsRecreationVisitor patternPsychologyHealth benefitsWell-beingSocioeconomicsMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Executive Summary: Leisure in parks and other forms of protected areas are connected to an individual’s health and well-being. In this paper, we report on the results of a multi-year study that surveyed 1,515 visitors to three Provincial Parks and three Kananaskis Country Provincial Recreation Areas in Alberta, Canada. Results revealed several important findings with significant policy and planning implications for Alberta Parks, as well as the international parks and protected area community more broadly. Findings show that anticipated human health and well-being benefits were a major factor motivating individuals’ decision to visit a park or protected area. Perceived psychological/emotional benefits (89.1% of visitors), social benefits (88.3%), physical benefits (80.3%) and environmental well-being benefits (79.4%) were deemed the most important motivations. However, there was a negative correlation between age and each of these perceived benefits, indicating that older visitors were less motivated to visit protected areas for these reasons. Perceived benefits (outcomes) followed a similar pattern to motivations. The most improved factors were psychological/emotional (90.5%), social (85%), and physical well-being (77.6%). A demographic analysis revealed that females rated financial, social, psychological/emotional and spiritual well-being motivations higher than males. Income and education were also positively related to individuals’ ratings of physical, psychological and environmental well-being. Interestingly, health motivations and benefits (or outcomes) were correlated highly with nature relatedness, meaning the more connected one is to nature, the greater the motivation to visit parks and the greater the health and well-being benefits received from park experiences. Overall, this study represents the largest examination of the human health and well-being benefits associated with visitor experiences in a Canadian protected areas context. The results substantiate the need for park organizations to better understand the “service provider” – “client” relationship from a human health and well-being perspective so that integrated policies and visitor experience programs can be developed or enhanced where appropriate. The Alberta Parks Division, and the international protected areas community more broadly, should actively develop the social science foundation internally, and externally (through partnerships with the social science research community), to ensure that decisions are science-based, society-oriented, and effective at meeting both conservation and visitor experience objectives. Finally, our research indicates the need for a better empirical understanding of the human health and well-being motivations and benefits of visitors representing different social and population subgroups (e.g., youth, elderly, couples, family units, new immigrants) and of the role of distinct natural environments in health promotion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.347
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2016
Admission routes2
Has abstractyes

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