How do athletes shift from self-critical to self-compassionate approaches within elite sport contexts?
Bibliographic record
Abstract
Self-compassion has been associated with greater psychological well-being and numerous adaptive responses in sport (Ferguson et al., 2015; Mosewich et al., 2013). Despite the well-documented benefits of adopting a self-compassionate approach, little is known about how athletes practically execute taking a more self-compassionate perspective in competitive settings. The purpose of this study was to explore how elite athletes shifted from a self-critical to a self-compassionate approach in managing challenges and concerns in sport. This study also investigated the barriers and facilitators athletes faced when practically integrating self-compassion within sport competition and training. A framework for interpretive description informed this study (Thorne, 2016). Eleven elite athletes (6 men and 5 women), who identified as being previously overly self-critical but currently manage criticism more effectively, participated in one-on-one semi-structured interviews. Interviews were audio-recorded, transcribed verbatim, and analyzed using six phases for data analysis outlined by Thorne (2016). Through engaging in thematic analysis (Thorne, 2016), five themes were developed to encompass participants' shifts toward self-compassion: (1) the role of the coach, (2) the influence of other athletes, (3) the impact of important others, (4) developing balanced self-awareness, and (5) maintaining an accepting mindset. Within each theme, elements that foster and inhibit athlete integration of a self-accepting approach are outlined. This study provides key insight into how athletes have practically integrated self-compassion within sport contexts, and may help facilitate more positive and adaptive experiences for future athletes, as well as guide future intervention efforts.Acknowledgments: Supported by the University of Alberta Roger S Smith Undergraduate Research Award
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".