Creating sport environments for youth to thrive: understanding the mechanism to intentions to continue sport and subjective well-being
Bibliographic record
Abstract
The study investigated how social environmental factors (i.e., supportive behaviors of coaches and parents) are associated with thriving in sport amongst a youth population, influencing important outcomes of sport participation (i.e., intention to continue sport and subjective well-being in sport). Two-wave data was collected within a three-month time span to help determine the relationship of social environmental factors with intention to continue and subjective well-being. One hundred fifty-nine Japanese youth sport participants (Mage = 15.81) completed the questionnaires twice. Autonomy supportiveness of coaches and parents was positively related to thriving three months later. In contrast, only the relationship between autonomy support from coaches and thriving was mediated by the change in basic psychological need satisfaction. Moreover, autonomy support from coaches was related to intention to continue sport and subjective well-being in sport through the changes in basic psychological needs satisfaction and thriving individually and in sequence. The results indicate the importance of both the parent and coach in the role of nurturing the young athletes, while the influence was more robust for coaches. The findings indicate a need for stakeholders to focus on educating and promoting their role in creating supportive environments aimed at increasing retention and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".