Predictors of eudaimonia and hedonia in organized sport
Bibliographic record
Abstract
This study was designed to identify the predictors of eudaimonia and hedonia in organized sport at varying levels of competition (from recreational to elite levels). These complimentary aspects of subjective well-being may influence athlete persistence and satisfaction in their sport. Eudaimonic well-being involves pursuing activities that reflect one's personal values and hedonic well-being is seeking comfort or enjoyment (Huta, 2013). The potential predictors examined included team environment, coach strategy and feedback, and components of self-determination. A secondary focus of this study was to examine the differences between the level of play an athlete participates in and hedonia and eudaimonia. Participants (N=55) completed a 133-item self-report questionnaire comprised of validated measures of hedonia and eudaimonia, intrinsic motivation, positive and negative affect, autonomy, athlete and coaching behaviour, and basic need support (autonomy, relatedness, and competence; Ryan & Deci, 2000). Significant correlations were observed between eudaimonia scores and personal dedication scores (r = .324) in the Athlete Satisfaction Questionnaire (ASQ; Riemer & Chelladurai, 1998). For hedonia scores, a significant correlation was observed with the team integration (r = .179) portion of the ASQ. No relationships were found between an athlete's level of play and eudaimonia or hedonia. The results of this study suggested that optimal well-being for athletes that play organized sport lies in their personal dedication to their sport, as well as being a contributing member to the progress of their respective team.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".