University Student-Athletes’ Experiences of Facilitators and Barriers to Contribution: A Narrative Account
Bibliographic record
Abstract
University student-athletes’ contributions in the form of volunteering, community engagement, and civic engagement have been the subject of recent research; however, no studies have specifically examined the factors that facilitate or serve as barriers to contribution in this population. As such, the purpose of this study is to explore the facilitators and barriers relating to university student-athletes’ contributions. Individual semi-structured interviews were conducted with eight university student-athletes (two males, six females) between 18 and 21 years of age (M = 19.25) from two Canadian universities. The analysis led to the identification of two qualitatively distinct profiles regarding how facilitators and barriers to contributions were experienced: (a) the first-year student-athletes and (b) the sustained contributors. Although the participants in each profile identified teammates, coaches, and athletics department staff as facilitators to contribution, they differed in their interpretation of how these individuals facilitated contributions. First-year student-athletes were more reliant than sustained contributors on having facilitators create contribution opportunities. The profiles also differed in regards to how time constraints were overcome. First-year student-athletes utilized less complex, individual time-management strategies, while sustained contributors collaboratively made use of more advanced time-management strategies to optimize their time.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".