University sport retirement and athlete mental health: a narrative analysis
Bibliographic record
Abstract
When sport participation reaches competitive levels, it can become entangled with stressors such as injury, performance pressures, high internal and external expectations, and difficult retirement transitions. Retirement can leave individuals vulnerable to experiencing mental health challenges, particularly when an athlete has developed a strong athletic identity. In this study, narrative inquiry philosophy informed an exploration of the experiences of Bryn. Bryn is an elite, female university athlete who developed an adjustment disorder with mixed moods of depression and anxiety after retiring from sport and graduating from university. Seven life history interviews were conducted and a dialogical narrative analysis was used to examine the influence of the structure of the sport context on Bryn’s experience of a challenging retirement transition. While she was an athlete, the success and recognition Bryn experienced in her sport community represented a powerful platform for developing self-confidence and a strong athletic identity. When this platform was removed upon retirement, and access to resource and support networks contingent on her star-athlete status were no longer available, Bryn had significant difficulty coping with threats to her mental health. The findings from this study lead us to question whether the significant support and special access to services provided to university sports stars may potentially leave such individuals vulnerable to feelings of isolation and helplessness once outside the university-athlete role.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".