Personal development in a high-performance sport environment
Bibliographic record
Abstract
Personal development is important to help athletes successfully deal with challenges and transitions throughout their career (Devaney et al., 2018). The purpose of this study was to develop a grounded theory of how to promote personal development in a high-performance sport environment. Interviews were conducted with 32 individuals who were involved in the Canadian national biathlon teams. The sample was comprised of 18 athletes (9 women, 9 men, Mage = 20.8 years, SD = 2.9), 5 coaches (1 woman, 4 men), 3 technical leaders (2 women, 1 man), and 6 parents (3 mothers, 3 fathers). Following Straussian grounded theory methodology (Corbin & Strauss, 2015), there was an iterative process of data collection and analysis. Analytic techniques included open coding, constant comparison, questioning, memoing, diagramming, and theoretical integration of concepts and categories. The grounded theory of personal development was built around the core category of continuous and individual The core category was underpinned by three categories: (a) psychological skills and characteristics, which referred to skills (e.g., realistic self-evaluation, goal-setting) and characteristics (e.g., hard-work ethic, independence) that promoted athletes' personal development; (b) social support systems, which involved emotional, esteem, informational, and tangible support; and (c) transitions and life lessons, which encompassed events that influenced the athletes' personal development. The theory predicts that athletes can experience personal development when they develop psychological skills and characteristics, use their social support systems, and learn from transitions and life lessons. The theory will be used to design a personal development program for high-performance athletes.Acknowledgments: During this study, the first author was supported by the PhD Students and Early Career Academics Research Grant Program 2021 from the The Olympic Studies Centre.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".