Achieving international podium success with a positive sport experience
Bibliographic record
Abstract
Positive coaching strategies (e.g., athlete – centred, autonomy – supportive, caring, transformational) have been highly regarded for their holistic approaches in creating supportive sport environments, enhancing motivation, and supporting athlete well – being. Despite calls for more positive approaches to athlete development, there is mounting evidence that high – performance coaches do not embrace these coaching styles, opting for controlling or emotionally abusive coaching styles. While various researchers have argued that a positive sports environment can lead to high – performance outcomes, coaches remain hesitant to change due to a perceived lack of solid evidence directly linking positive coaching methods to improved performance. This study addresses this gap by exploring international medal – winning athletes’ and coaches’ perspectives of achieving performance results in positive sport environments. Using a constructivist approach, semi – structured interviews were conducted with 24 Olympic and Paralympic athletes (n = 13) and coaches (n = 11) who had won a medal at an Olympic, Paralympic, Pan American Games, or World Championship. Results suggest that medals can be obtained in positive sport environments. Participants reported positive sport experiences also increased positive emotions, motivation, resiliency, feelings of being cared for and valued, personal development, sport – life balance, and staying in sport longer. All of these outcomes further contributed to increased performance results. These findings provide empirical evidence to support previous assertions that positive sport experiences can contribute to high – performance success.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".