What is an athlete's psychological well-being? Constructing concepts with Olympic and Paralympic athletes
Bibliographic record
Abstract
Although psychological well-being (PWB) in athletes is receiving increased scrutiny, there is some confusion about what constitutes PWB in elite sport. Psychological well-being (eudaimonia) is associated with purpose, growth, and fulfilling one’s potentials (Aristotle, 4c BCE, 1985); however, there are limitations in the conceptual clarity of PWB, and in the context of elite sport global frameworks of PWB have been used to study context-specific (athlete) PWB. Important research questions remain as there is little understanding of what constitutes athlete PWB. The objective of this study was to explore contextually relevant components of athlete PWB and to gain insight on the make-up of athlete PWB. Semi-structured interviews and photograph elicitation were employed at two time points with 26 athletes (7 Paralympic; 19 Olympic) from four countries (Canada; Great Britain; Australia; New Zealand), and the data were analysed using a reflexive thematic analysis (Braun & Clarke, 2019). Eight components of sport-specific PWB were constructed to represent athletes’ understandings of PWB in elite sport. The eight components included sport-confidence and self-worth; personal balance; aligned purpose; fulfillment; performance; personal agency; psychologically safe relationships; and psychological adaptability. Developed through an interpretive theoretical process, these components maintain the core aspects of eudaimonia but extend to highlight the contextually relevant components of sport-specific PWB. This provides a more meaningful and applicable representation of athletes’ experiences of PWB.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".