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Record W7118018062 · doi:10.17605/osf.io/4dmpf

Orchids at the Gym: Investigating Relationships Among Sensory Processing Sensitivity, Preferred Physical Activity Environments, and Physical Activity Self-Efficacy in University Students in Ontario

2024· other· W7118018062 on OpenAlexaboutno aff
Shauna Burke, B. Hanson, Trish Tucker, Jennifer Irwin

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityTraitNoticePerceptionPersonalityPopulationVariety (cybernetics)Big Five personality traits

Abstract

fetched live from OpenAlex

Study Summary Because the personality trait of sensory processing sensitivity (SPS) enhances individuals’ ability to notice subtle environmental details, it often affects individuals’ preferred environments to engage in behaviours, and may also influence self-efficacy to overcome environmental barriers and perform these behaviours. One specific behaviour of interest is physical activity (PA), which leads to a variety of physical and mental health benefits. However, many environmental barriers to PA exist, especially among university students in Canada, which is the population this study will focus on. This study hypothesizes that perceptions about the suitability of PA environments, as well as PA-specific self-efficacy, will be influenced by the SPS trait in Ontario university students. The primary objective of this study is to investigate whether relationships exist between SPS level and various PA-related environmental preferences (e.g., social setting, location, noise levels) of Ontario undergraduate university students. As a secondary objective, this project aims to explore whether a relationship exists between these students’ SPS levels and their PA-specific barriers self-efficacy.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.007
Science and technology studies0.0030.010
Scholarly communication0.0030.005
Open science0.0040.017
Research integrity0.0010.012
Insufficient payload (model declined to judge)0.0000.001

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 head0.067
GPT teacher head0.327
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

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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