Self-compassion in sport: a scoping review
Bibliographic record
Abstract
Sport is a domain that is rife with loss, failures, and disappointment. Self-compassion – the recognition of one’s own suffering and a desire to alleviate it – offers protection against maladaptive psychological experiences in sport. The purpose of this scoping review was to update and expand the results of the review by Röthlin and colleagues ([2019]. Go soft or go home? A scoping review of empirical studies on the role of self-compassion in the competitive sport setting. Current Issues in Sport Science, 4, Article 013. https://doi.org/10.15203/CISS_2019.013), and to identify new themes to help guide future research. Sixty-nine publications were identified using a variety of search strategies. Quantitative research (62.3%) and cross-sectional designs (83.3%) were most common, and most research was conducted by researchers residing in Westernized countries (81.2%). The majority of study participants (n = 10,025) were collegiate athletes (42.1%), and female/women sport participants were sampled slightly more frequently (52.4%). Researchers often investigated sex- or gender-based and competition level differences in self-compassion scores. Other common areas of research focus included well-being, mindfulness, striving for excellence, overcoming setbacks, negative thoughts and emotions, and self-criticism. New research areas that were identified included a need for theory, additional efforts towards conceptualization and measurement, acknowledgement of participant selection bias, integrating intersectionality, the relationship between self-compassion and performance, the distinctiveness between self-compassion and mindfulness, and future directions for interventions.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".