Coach Burnout in Relation to Perfectionistic Cognitions and Self-Presentation
Bibliographic record
Abstract
Coaching athletes is highly rewarding yet stressful, especially at the elite level where media, fans, and sponsors can contribute to an environment that, if not well-managed by the coach, can lead to burnout. Coaches who display perfectionistic tendencies, such as striving for flawlessness, may be particularly vulnerable-even more so if they are overly critical of themselves and have a tendency to ruminate over their performance, or if they are attempting to convey an image of faultlessness, or both. A total of 272 coaches completed a battery of inventories assessing burnout, perfectionistic thoughts, and the tendency for perfectionistic self-presentation. All variables correlated significantly: coaches with higher scores on exhaustion scored higher both on perfectionistic thoughts and self-presentation. However, when three subscales of perfectionistic self-presentation were considered separately, lower and nonsignificant correlations emerged. We believe that this can be explained by the heterogeneous group of coaches participating in this study. Whereas all coaches may at times ruminate privately-self-oriented perfectionism-about their perceived failure to perform to expectations, not all may feel the pressure to present themselves to others as faultless-a more socially prescribed perfectionism. This finding warrants further investigation, preferably comparing coaches at different levels of public scrutiny.
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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 | 0.010 |
| 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".