“It’s hard to define and really hard to implement”: Competitive women athletes’ descriptions of self-compassion.
Bibliographic record
Abstract
Research on self-compassion as an important resource for women athletes is increasing at an impressive rate. However, there can be misunderstandings about what self-compassion is and is not. This is perhaps not surprising given that self-compassion is not part of most athletes’ common vernacluar. The best language to use when talking about self-compassion with women athletes remains unclear. The purpose of this qualitative description study was to explore women athletes’ understandings of self-compassion, particularly their language used to describe the construct. Competitive women athletes (N =19; Mage = 22.6 years, SD = 5.4) were invited to participate in two phases of virtual focus groups. Phase 1 generated information regarding women athletes’ descriptions of self-compassion. Elo and Kyngäs’ (2008) content analysis was used to prepare, organize, and report the data into content-specific themes. Preliminary themes were shared with participants in Phase 2 (11 of the original 19 participants returned), after which all focus group transcripts (i.e., Phase 1 and Phase 2) were (re)analyzed using the same analytic approach. Three themes were generated: (a) Show up (driven by empowerment, supporting myself as I support others), (b) Regroup (honestly checking in with myself for real expectations), and (c) Trust (trusting the process and trusting myself). The language used by participants to describe self-compassion incorporates elements of both tender (i.e., comforting reassurance) and fierce (i.e., protecting and providing) forms of self-compassion. Findings provide relevant and useful information for researchers, applied practitioners, and sport personnel seeking to communicate with women athletes about self-compassion.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".