Self-compassion and psycho-physiological reactivity and recovery from recalled sport failure
Bibliographic record
Abstract
Sport failure can precipitate emotional distress that is detrimental to athletes’ mental health and performance. Self-compassion (SC), treating oneself kindly in response to failure, may help athletes manage failure; it buffers against negative psychological responses, yet athletes often fear SC. It is unknown whether the benefits of SC extend to athletes’ physiological responses to failure and whether fear of self-compassion (FOSC) has an influence beyond SC. This study’s purpose was to examine SC’s influence on athletes’ psychological and physiological responses to a sport failure, and determine if FOSC exerted unique effects, beyond SC. Participants (M age=21) in this laboratory-based, observational study were 91 university or national-level athletes. A multi-modal biofeedback system was used to measure physiological responses at baseline, while imagining a past performance failure (reactivity), and during recovery. Physiological responses were assessed according to participants’ i) reactivity and ii) recovery phases, relative to baseline scores. Psychological responses were assessed using behavioural reactions, thoughts, and emotion measures. Regression analyses revealed that SC predicted athletes’ physiological recovery, in the form of heart rate variability (β = .37, p < .01) but not their reactivity. SC associated with adaptive behavioural reactions (β = .46, p < .01), and negatively related to maladaptive thoughts (β = -.34, p < .01) and emotions (β = -.39, p < .01). FOSC explained additional variance in maladaptive thoughts and behaviours. SC may promote adaptive physiological and psychological responses in athletes relative to recalled sport failures and may have implications for performance, recovery and health.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".