Perfectionism and Burnout Within Intercollegiate Sport: A Person-Oriented Approach
Bibliographic record
Abstract
This study investigates the functional nature of perfectionism in sport through a person-oriented comparison of healthy and unhealthy perfectionist athletes’ levels of burnout. A sample of 117 intercollegiate varsity student-athletes (M age = 21.28 years, SD = 2.05) completed measures that assessed multidimensional sport-based perfectionism and athlete burnout indices (i.e., reduced accomplishment, sport devaluation, and emotional/physical exhaustion). Cluster analysis revealed that the sample could be represented by four theoretically meaningful clusters: Parent-Oriented Unhealthy Perfectionists, Doubt-Oriented Unhealthy Perfectionists, Healthy Perfectionists, and Non-Perfectionists. Intercluster comparisons revealed that healthy perfectionists reported (a) lower levels on all athlete burnout indices in comparison with both doubt-oriented unhealthy perfectionists and nonperfectionists and (b) lower levels of emotional/physical exhaustion in comparison with parent-oriented unhealthy perfectionists (all ps < .05). The degree to which findings fit within perfectionism/burnout theory and can serve as an example for research with enhanced relevancy to applied sport psychology contexts is discussed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".