Investigating the Relationship Between Campus Characteristics and Help-seeking Behaviours in Canadian Varsity Student-Athletes
Bibliographic record
Abstract
Compared to the general population, university student-athletes exhibit mental health issues at a similar or higher rates (Sullivan et al., 2019; Wolanin et al., 2016). Despite this, research suggests that help-seeking rates for athletes continue to remain significantly lower than their non-athlete peers (Edwards & Froehle, 2023). Stigma, sport culture and other contextual factors may be significant reasons for these help-seeking trends in university athletes (Uphill et al., 2016). This research aims to investigate these contextual factors by exploring help-seeking behaviours of varsity student-athletes as they relate to campus characteristics. This study analyzed 52,326 (1,845 varsity) students in the Canadian subset of the National College Health Assessment (NCHA) from data collected in the Spring 2019. Descriptive statistics and logistic regression models compared campus characteristics and varsity status as they relate to help-seeking rates. Results indicate significantly lower rates of general (40.7% vs. 46.5%) and future (76.9% vs. 80.0%) help-seeking for varsity students. However, campus help-seeking rates are slightly higher for varsity students (21.1% vs. 19.9%). Additionally, varsity athletes were 38.7% less likely to seek help on larger campuses (>20k students) compared to smaller campuses (
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".