Athlete burnout in elite sport: A self-determination perspective
Bibliographic record
Abstract
Using self-determination theory (Deci & Ryan, 1985) as the theoretical framework, we examined potential antecedents of athlete burnout in 201 elite Canadian athletes (121 females, 80 males; mean age 22.9 years). Employing a cross-sectional design, our primary aims were to investigate the relationships between behavioural regulations and athlete burnout and to examine whether self-determined motivation mediated relationships between basic needs satisfaction and athlete burnout. Our self-determination theory-derived hypotheses were largely supported. Relationships among athlete burnout and behavioural regulations mostly varied according to their rank on the self-determination continuum, with less self-determined motives showing positive associations and more self-determined motives showing negative correlations with burnout. The basic needs of competence and autonomy, plus self-determined motivation, accounted for significant amounts of variance in athlete burnout symptoms (exhaustion, R(2) = 0.31; devaluation, R(2) = 0.49; reduced accomplishment, R(2) = 0.61; global burnout, R(2) = 0.74). Self-determined motivation fully mediated the relationships that competence and autonomy had with exhaustion. Analyses showed indirect relationships between these two needs and devaluation, through their associations with self-determined motivation. Motivation partially mediated the needs-reduced sense of accomplishment relationships, but the direct effects were more prominent than the indirect effects.
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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 | 0.002 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".