"My Toughest Opponent Was Myself": Exploring Personality and Self-Compassion in Coping with Athlete Performance Slumps
Bibliographic record
Abstract
Performance slumps are prolonged periods of unexplained underperformance that exceed an athlete’s typical fluctuations and affect over 50% of athletes, often leading to heightened stress, anger, and frustration. While the expression of slumps can vary depending on sport type (individual versus team) and personal factors, how athletes cope with them, and how these factors influence coping, remains less understood. This study aimed to explore the relationships between sport type, personal factors (personality and trait self-compassion) and coping strategies used during performance slumps. We recruited 184 athletes from Canada, the United States, the United Kingdom, and Australia to complete a quantitative survey that assessed demographics, sport and slump history, personality, self-compassion, and coping. Linear regression analyses revealed significant relationships between personal factors and each category of coping strategy (problem-focused, emotion-focused, and avoidance). Specifically, extraversion and conscientiousness predicted greater use of problem-focused strategies, while neuroticism and mindfulness predicted greater use of emotion-focused strategies. Additionally, agreeableness and conscientiousness predicted less use of avoidance strategies. These patterns are notable as problem-focused and emotion-focused strategies are generally considered more adaptive and linked to more positive outcomes, whereas avoidance strategies are typically viewed as more maladaptive and linked to more negative outcomes. Despite these results, there were no significant differences in coping strategy use between individual-sport athletes and team-sport athletes. Together, these findings underscore the importance of personality and self-compassion in coping with performance slumps, highlighting the need for future research to examine and develop support strategies tailored to individual differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".