Growing pains: Exploring negative experiences and positive growth among elite female athletes
Bibliographic record
Abstract
The purpose of this study was to explore negative experiences and positive growth among elite female athletes. Multiple interviews were conducted with five elite female athletes (M age = 20yrs) who competed nationally and internationally in their respective sports. Interviews were analyzed using a phenomenological approach (Groenewald, 2004; Smith & Osborn, 2008). Idiographic profiles were created to examine the meaning of negative experiences for each athlete, and common themes were examined across the athletes' profiles. Participants described diverse competitive (injury, sport transitions, conflict with coach) and non-competitive (bullying, eating disorder, sexual abuse) experiences which affected their athletic careers. Athletes' experiences were framed as part of an ongoing journey through elite sport which was characterized by perceived expectations and impression management. Within this context, the essential features of athletes' negative experiences were explored (isolation/withdrawal, emotional disruption, questioning identity as an athlete). Positive growth occurred for these athletes if they risked losing sport in their lives during their negative experiences. Findings highlighted the overlap between negative non-competitive experiences and athletes' 'sporting' selves. The results of this research have implications for understanding the ways in which elite athletes may or may not draw benefits from negative experiences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".