Motivation and eudaimonic well-being in athletes: A self-determination theory perspective
Bibliographic record
Abstract
Drawing from self-determination theory (SDT; Deci & Ryan, 1985), the present study examined the relationship between motivation and eudaimonic well-being in the sport context. All the types of motivation were tested individually to examine how they influence athletes' eudaimonic well-being via Structural Equation Modeling (SEM). Three hundred ninety nine athletes (Mage = 25.08, SD = 7.35) from 15 different individual and team sports completed a questionnaire tapping the targeted variables. The analysis partially supported the hypotheses. Integrated and identified regulations positively predicted athletes' eudaimonic well-being. External regulation was also a positive predictor of the eudaimonic well-being, while introjected regulation and amotivation negatively predicted athletes' eudaimonic well-being. Finally, athletes' intrinsic motivation did not significantly predict their eudaimonic well-being. Results highlight the complex link between different types of sport motivation and athletes' well-being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".