Levels of mental distress in a national sample of Canadian university student athletes
Bibliographic record
Abstract
Mental health concerns for post-secondary students in Canada are well documented. Less well known is the mental health status of student-athletes. A recent study revealed that student athletes in the United States had lower levels of anxiety and depression than their non-athlete counterparts (Davoren & Hwang, 2014). However, there are significant differences between American and Canadian university sport systems that would limit any generalizations from these results. The current study was designed to ascertain the prevalence of mental distress in Canadian university sport participants. A nation-wide sample of 305 university student athletes (188 female; 117 male) complete the K6, a brief scale for screening of general psychological distress (Kessler et al., 2002). The sample was heterogeneous with respect to sport, region and timing of competitive season. The results showed that student athletes report relatively high levels of mental distress. The average score on the scale for the current sample was 14.3 (out of 24), whereas a comparable national average for the same age range is 4.3 (Cairney et al., 2007); 59% of the sample indicated a score above the optimal cut off for assessing prevalence of severe mental illness. There were no differences by gender or other demographic variables, however, it was found that individuals with a history of concussion showed significantly lower levels of mental distress than those without (t (270) = -1.99, p < .05, d = 0.23). It should be noted that the K6, although valid, is a screening tool, not a diagnostic tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".