Letting Go of Gold: Examining the Role of Autonomy in Elite Athletes’ Disengagement from Their Athletic Careers and Well-Being in Retirement
Bibliographic record
Abstract
Retirement from competitive sports significantly influences former athletes’ well-being. We propose that disengaging from the former athletic career is a crucial factor in retired athletes’ adaptation. Using the theoretical framework of Self-Determination Theory (SDT) we propose that sport motivation at the career peak and motivation for retirement are important determinants of athletes’ disengagement progress from a terminated athletic career. We also seek to examine how motivation for retirement and disengagement progress predict retired athletes’ well-being. Using a mixed-retrospective/prospective longitudinal design we followed 158 government-supported elite athletes who had recently retired from an athletic career. In two online surveys administered 1.5 years apart, retired athletes reported on motivation, disengagement, and well-being. Results suggested that SDT motivation factors are important predictors for elite athletes career disengagement and well-being in retirement. The clinical implications of these findings for athletic career transition and support programs are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".