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Record W3119123761

Perception of Recreational Opportunities on a Campus to Increase Wellness

2018· article· en· W3119123761 on OpenAlexaffabout
Brittni Steeves

Bibliographic record

VenueURSCA Proceedings · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsRecreationPerceptionPsychologyApplied psychologyMedical educationOutdoor educationPedagogyMedicineEcology
DOInot available

Abstract

fetched live from OpenAlex

Universities strive to enhance the student experience and promote student success. A major contributor to student success is the development of wellness, defined as the state of being in good health, especially as an actively pursued goal. Positive subjective wellness is achieved when individuals actively engage leisure activities to increase wellness. In conjunction with the Lethbridge Campus Ecology Project, the purpose of the study was to investigate undergraduate students’ use of campus green space to increase wellness and manage perceived stress. To enhance wellness in practice, it  is essential to determine not only what the students want, but also what types of outdoor leisure additions they would use. A survey was generated to explore students’ perception of leisure opportunities on campus and investigate what type of outdoor recreation students would use to enhance their wellness. Information gathered from responses will provide insight on what implementations would be utilized to increase greater wellness and reduce stress in students. It is hypothesized that outdoor leisure opportunities will be associated with an increase in subjective student wellness. Additionally, it is expected that females and males will differ significantly in their choices for wellness enhancement strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.031
GPT teacher head0.265
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes2
Has abstractyes

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