Unexpected, but not a surprise: How women varsity athletes high in self-compassion manage unexpected stressors surrounding competition
Bibliographic record
Abstract
Athletes appraise unexpected stressors as more threatening than expected stressors (Dugdale et al., 2002) and women varsity athletes have reported experiencing a high proportion of unexpected competition-related stressors (Holt et al., 2007). Self-compassion may promote adaptive appraisals and coping in women athletes (Mosewich et al., 2018), and a self-compassionate perspective may aid athletes in navigating the experience of unexpected stressors. Therefore, the purpose of this study was to explore how women varsity athletes high in self-compassion manage unexpected stressors surrounding competition. Based on Self-Compassion Scale scores (Neff, 2003), seven women varsity athletes (Mage = 19.43 years, SD = 1.40 years) high in self-compassion (M = 3.83, SD = 0.48) were purposefully sampled to participate in one-on-one semi-structured interviews. Through an interpretive description framework (Thorne, 2016), four themes were developed that illustrate coping efforts: Emotion regulation (effectively controlling emotional responses to the stressor), adaptive perspective (taking a balanced, objective approach to the situation), active re-engagement (approaching and engaging with the task at hand), and learning from experience (drawing on, and learning from, past experiences). It appears that varsity women athletes with high self-compassion possess resources that enabled them to effectively cope with unexpected stressors. To support athletes in managing unexpected stressors, coaches and practitioners can encourage athletes to reflect on past experiences, support emotion regulation strategies, and foster adaptive perspectives to aid athletes in effectively engaging with and managing unexpected stressors surrounding competition.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".