The Associations Among Self-Compassion, Self-Esteem, Self-Criticism, and Concern Over Mistakes in Response to Biomechanical Feedback in Athletes
Bibliographic record
Abstract
Athletes regularly face the possibility of failing to meet expectations in training and competition, and it is essential that they are equipped with strategies to facilitate coping after receiving performance feedback. Self-compassion is a potential resource to help athletes manage the various setbacks that arise in sport over and above other psychological resources. The primary purpose of this research was to explore how athletes respond to objective biomechanical feedback given after a performance. Specifically, we investigated if levels of self-compassion, self-esteem, self-criticism, and concern over mistakes were related to one another before and after a series of sprint tests interspersed with biomechanical feedback, and whether self-compassionate athletes achieved a better sprint performance after receiving and implementing biomechanical feedback. Forty-eight athletes (20 female: Mage = 19.8 years, SD = 3.1; 28 male: Mage = 23.6 years, SD = 7.8) completed online measures of self-compassion, self-esteem, self-criticism and concern over mistakes before performing four sets of 40-m sprints. Participants received personalized biomechanical feedback after each sprint that compared their performance to gold standard results. Following all sprints, they then completed measures of self-criticism, and reported emotions, thoughts, and reactions. Self-compassion was positively correlated with self-esteem (r = 0.57, p < 0.01) and negatively related to both self-criticism (r = −0.52, p < 0.01) and concern over mistakes (r = −0.69, p < 0.01). We also found that athletes with higher levels of self-compassion prior to sprint performance experienced less self-critical thoughts following biomechanical feedback and subsequent sprint trials (r = −0.38, p < 0.01). Although the results of this study provide some support for the effectiveness of self-compassion in promoting healthy emotions, thoughts, and reactions in response to sprint performance-based biomechanical feedback, a moderated regression analysis between the first and fourth sprint time variables revealed that self-compassion was not a moderator for change in sprint performance (R2 = 0.64, ΔR2 = 0.10, p > 0.05). These findings suggest that there are likely longer-term benefits of athletes using self-compassion to cope with biomechanical feedback, but that any benefits might be limited in a short series of sprint trials.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".