Composite vignettes of challenges faced by Canadian student-athletes negotiating the demands of university life
Bibliographic record
Abstract
Background: Student-athletes must balance demanding athletic, academic, and social roles, producing distinct challenges compared to other student populations. Objective: The purpose of this study was to explore and represent through a creative medium the challenges faced by Canadian university student-athletes associated with managing the demands of their athletic, academic, and social roles. Method: Data were collected using semi-structured interviews with 20 collegiate student-athletes from Canadian institutions who reflected on adverse experiences in academic, athletic, and social domains. Data analysis consisted of inductive thematic analysis, and the creation of composite vignettes to represent findings. Composite vignettes are a form of non-fiction storytelling fusing together accounts of multiple participants into narratives depicting key findings. Results: Five composite vignettes were created to depict thoughts and experiences described by participants relating to challenges of being a student-athlete. Each vignette begins with third-person description of their immediate setting, followed by an internal monologue reflecting on their circumstances. Conclusion: Findings are presented as vignettes to appeal to a broad audience, and to facilitate readers drawing upon their own experiences to form personally-relevant conclusions. The authors' interpretations and perspectives on practical implications are also offered.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".