A story of loss and gain: Exploring student-athletes' experiences with injury through photovoice
Bibliographic record
Abstract
Athletes' experiences with injuries is a broad and widely studied topic (Granito, 2002; Lu & Hsu, 2013; Wadey et al., 2011). However, little is known about the impact injuries have on student-athletes. The unique experiences associated with student-athletes' need to balance both a strong athletic identity and an academic career provides a novel perspective in the area of athletic injury. Using the photovoice method along with semi-structured interviews, the aim of this study was to capture student-athletes' experiences of serious injury. The study looked at nine recently-injured, competitive athletes from various Varsity sports. Themes and patterns concerning loss of identity, balance, and freedom, as well as gaining new appreciation, a different perspective, and stronger social support emerged from both the pictures and interviews. These new insights on the emotional and psychological experiences of injured student-athletes will serve to fill the gap in the current litterature as well as inform coaches, teammates, consultants, and clinicians on the deeper impacts of injury and how to help minimize the losses and maximize the gains.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".