Smartphones and Varsity Athletes: A Complicated Relationship
Bibliographic record
Abstract
Varsity athletes are a group of high performers situated within a demographic notable for smartphone usage and media-multitasking. Surprisingly, little research has examined the impact of smartphones in the lives of varsity athletes. The purpose of this exploratory, qualitative study was to begin addressing this gap by investigating varsity athletes' experiences with smartphones. Varsity athletes ( n = 21) from nine different sports participated in one of five focus groups, and data emerging from these discussions were subjected to an inductive thematic analysis. Results indicate that smartphones are a mainstay of varsity athletes' experiences, as the athletes regularly use their smartphones to manage roles and demands across multiple contexts (e.g., sport, school, home). Themes pertained to concurrent negative (e.g., stress, distraction, disengagement) and positive (e.g., self-regulation, social connectedness) implications of smartphone usage, making it clear that athletes' relationship with their smartphone is a complicated one. Findings contribute to the limited studies of smartphone usage among athletes, and support the notion that implications of usage exist along a continuum, rather than in distinct categories of “good” and “bad”. Results can inform practical guidelines for optimising athletes' use of smartphones in and around the sport context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".