Narratives of young women athletes’ experiences of emotional pain and self-compassion
Bibliographic record
Abstract
Self-compassion is a healthy way of relating to the self when experiencing emotional pain, personal failure and difficult life experiences. However, there is limited research to date in the area of self-compassion and sport even though recent investigation shows it might act as a potential buffer to painful emotions for athletes. The purpose of this study was to explore and present narratives of six young women athletes (15–24 years) from a variety of sports about their experiences of emotional pain and self-compassion. Each woman took part in two individual semi-structured interviews, one of which involved reflexive photography. They were asked to reflect on a difficult experience with a personal failure in sport, followed by discussions around the potential role of self-compassion in their experiences. The interviews, combined with reflexive photography, helped build rich narratives organised around the following themes: (1) Broken bodies, wilted spirits, (2) why couldn’t it have been someone else? (3) I should have, I could have, I would have and (4) fall down seven, stand up eight. Their narratives also suggested that while self-compassion can potentially be beneficial for athletes if developed and learned properly, concerns were expressed that being too self-compassionate may lead to mediocrity. Further research is needed on young women athletes’ difficult emotional experiences in sport, and more specifically on the role that self-compassion plays as both a potential facilitator and barrier to emotional health and performance success in sport.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".