Accessibility of Online Information About Student Post-Secondary Physical Health Activities and Initiatives on North American Campuses
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".