Crònica legislativa de la Unió Europea. Primer semestre de 2021
Bibliographic record
Abstract
Compared to non-athletes, student-athletes have shown less help-seeking behaviors and more resistance towards counselling services (Donohue et al., 2018). Specific contextual factors may be significant reasons for these help-seeking trends including social support and institutional/organizational characteristics (Mcleroy et al., 1988; Tashkandi et al., 2022). Additionally, COVID-19 appears to have had mental health ramifications for post-secondary student-athletes, as well as the larger student population (Dragioti et al., 2022). Therefore, the current study aimed to assess student-athlete help-seeking behaviours both before and after COVID-19 within the context of varsity status and campus characteristics. This study utilized the Canadian subset of the National College Health Assessment (NCHA) from data collected in the Spring 2019 (52,326 students) and the spring 2022 (10,870 students). Self-reported general, campus and future help-seeking rates for both varsity and non-varsity students were compared with descriptive statistics and logistic regression models. Results indicated that while varsity athletes had significantly lower general help-seeking rates in 2019 (40.71% vs. 46.51%), these rates were equal in 2022 (49.4% vs. 49.6%). Campus help-seeking rates while not significantly different in 2019 (21.1% vs. 19.9%) show significantly higher rates for varsity athletes in 2022 (21.1% vs. 12.4%). Lastly, students at 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".