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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".