MétaCan
Menu
Back to cohort
Record W4385843444 · doi:10.15273/hpj.v3i2.11518

Leveraging Exercise is Medicine On Campus Programs to Promote Activity to Equity-Deserving Groups

2023· article· en· W4385843444 on OpenAlexaff
M. Lauren Voss, Myles W. O’Brien, Joyla A. Furlano, Michelle Y. Wong, Nick W. Bray, Jonathon R. Fowles, Taniya S. Nagpal

Bibliographic record

VenueHealthy Populations Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of AlbertaAcadia UniversityUniversity of CalgaryWestern UniversityNova Scotia Health AuthorityUniversity of TorontoDalhousie UniversityMcMaster University
Fundersnot available
KeywordsPhysical activityPromotion (chess)Equity (law)Health promotionPandemicPerspective (graphical)GerontologyPolitical scienceHealth equityMedicineMedical educationPsychologyCoronavirus disease 2019 (COVID-19)Public relationsDiseasePublic healthNursingPhysical therapyInfectious disease (medical specialty)Politics

Abstract

fetched live from OpenAlex

Despite the well-known benefits of leading an active lifestyle, global adherence to physical activity recommendations is low. Individuals who are from marginalized groups, including racialized populations and those with a low socio-economic status, are more physically inactive compared to those who identify as white or who have a higher income. The differences in physical activity level by socio-demographic characteristics reflect inequitable access to lifestyle resources. Exercise is Medicine On Campus (EIM-OC) is a unique international post-secondary initiative that aims to promote a culture of physical activity and chronic disease prevention and management on university/college campuses and within their local communities. EIM-OC programs currently exist on every continent, with the majority of chapters existing in North America. This provides EIM-OC a unique opportunity to address inequities in physical activity promotion. This commentary provides perspective on traditional EIM-OC program offerings, highlights learnings from the COVID-19 pandemic, and recommends strategies to increase the inclusivity of future physical activity programming.

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)0.001
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 score0.926
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.266
GPT teacher head0.462
Teacher spread0.196 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHealthy Populations JournalSame topicPhysical Activity and HealthFrench-language works237,207