An athlete's experience learning and incorporating mindfulness and self-compassion in a high performance sport context
Bibliographic record
Abstract
For psychological skills to have performance benefit they must be successfully integrated into the training and competition context. Using a case study approach and a phenomenological orientation (Ravn & Christensen, 2014), we explored the experiences of an elite female athlete engaging in a mindfulness and self-compassion training program. Our purpose was to better understand, from an athlete's perspective, the process of learning and integrating mindfulness and self-compassion into an existing high performance training and competition routine. The six week program involved daily skills practice and weekly consulting sessions related to applying mindfulness and self-compassion to training and competition. Skill development and integration were a focus. One-on-one interviews (incorporated pre-, mid-, and post-program, and after the next major competition) provided a medium to explore the athlete's experience and a means for evaluation and self-reflection. Prior to the program, the athlete articulated goals around improving attention, awareness, emotion regulation, and management of other's expectations. At the mid-point, she shared her new perspectives around the impact of emotions and the acceptance of a different approach and cited an improvement in accountability, self-reliance, and awareness. By program conclusion, she perceived more adaptive emotion regulation and increased acceptance of emotion. Ongoing program evaluation supported implementation, identified facilitators (e.g., coach involvement, connection to established skills), and addressed challenges (e.g., time, frustration, distracting environment) identified by the athlete. This case study provides insights into the development and use of self-compassion and mindfulness in high performance sport, and may help inform strategies to support integration of such skills.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".