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Record W4401814148 · doi:10.18666/jpra-2024-12337

Qualitative Evidence to Inform Municipal Government Actions to Increase Recreation Space Usage and Promote Equitable Participation in Leisure-Time Physical Activity

2024· article· en· W4401814148 on OpenAlexaff
Ana Paula Belon, Krystyna Kongat, Laura Nieuwendyk, Helen Vallianatos, Candace I. J. Nykiforuk

Bibliographic record

VenueJournal of Park and Recreation Administration · 2024
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRecreationSocializationBusinessPublic relationsEquity (law)Intrapersonal communicationGovernment (linguistics)Inclusion (mineral)Focus groupMarketingInterpersonal communicationPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Municipal recreation infrastructure is critical for enhancing people’s opportuni-ties to be physically active during leisure time and reducing usage inequities. Yet, the literature on municipal equity-sensitive strategies is limited. To address this gap, we combined a socio-ecological framework with equity, diversity, and inclu-sion (EDI) and health equity lenses to examine intersecting social and structural determinants of municipal recreation (indoor and outdoor) infrastructure usage. We used data from 22 focus groups involving youth, adult regular users, and adult non-regular users. The intersecting factors influencing people’s utilization experi-ences were organized at intrapersonal, socio-cultural environment, organization, and built and natural environment levels. Policy and practice implications for mu-nicipal governments vis-à-vis EDI and health equity considerations are discussed. Recommendations include public transportation improvement, design of strategic communication plans, and linking activities with opportunities for socialization, among others.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.870
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.099
GPT teacher head0.468
Teacher spread0.369 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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