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Record W4413176059 · doi:10.3390/youth5030085

Accessibility of Online Information About Student Post-Secondary Physical Health Activities and Initiatives on North American Campuses

2025· article· en· W4413176059 on OpenAlexaff
Jonah Kynan Murray, Sarah Knudson

Bibliographic record

VenueYouth · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHealth informationPublic relationsPolitical scienceMedical educationMedicineHealth care

Abstract

fetched live from OpenAlex

Physical activity has been shown to improve the wellbeing of young adults pursuing post-secondary education, yet most college students do not perform adequate amounts of physical activity. Given post-secondary students’ reliance on internet and social media for physical activity information gathering, we questioned whether a lack of activity-promoting information might contribute to the activity deficit. Thus, we sought to determine the accessibility and extent of online physical health activity information and initiatives across a sample of large North American campuses by performing a series of physical wellness related searches through Google, the institutions’ sites, and the institutions’ Instagram accounts. Specifically, we question the extent of limitations to the accessibility and content information on institutions’ sites. We found less than half of all web searches and only three-point-five percent of social media posts had topically relevant information. Google was a more effective tool for finding relevant information than institution websites, suggesting institutions could benefit from improving access to physical activity information on their sites. Social media posts were primarily varsity and sport related, indicating a need for increased content about accessible physical activity options. Investigating possible directions to improve institution website usability could benefit student access to physical wellness resources on campus.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.544

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 studies0.0010.000
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 head0.031
GPT teacher head0.448
Teacher spread0.418 · 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
Published2025
Admission routes1
Has abstractyes

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