Need Satisfaction, Well-Being, and Perceived Return-to-Sport Outcomes Among Injured Athletes
Bibliographic record
Abstract
The purpose of this investigation was to examine whether components of psychological well-being (i.e., positive affect, negative affect, self-esteem, and vitality) mediated the relationship between self-determination theory (SDT) basic needs (competence, autonomy and relatedness) and perceived return-to-sport outcomes. Competitive athletes (n = 204) from Australia, Canada, and the United States completed an injury need satisfaction scale, psychological well-being inventories, and a measure of perceived return-to-sport outcomes. Mediation analysis (Baron & Kenny, 1986 Baron, R. M. and Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51: 1173–1182. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) revealed that positive affect partially mediated the relationship between competence and autonomy need satisfaction and a renewed perspective on sport, while negative affect, self-esteem, and vitality fully mediated the relationship between relatedness need satisfaction and return concerns. Interpretation of the findings suggests the importance of components of well-being in mediating relatedness need satisfaction on “return concerns” in a sport injury context. Prospective longitudinal designs using an SDT framework are discussed to further research in this area.
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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 | 0.005 |
| 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 | 0.001 | 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".