Perfectionist athletes' experiences with success and failure: An exploratory study
Bibliographic record
Abstract
The distinction between unhealthy and healthy perfectionism is a controversial issue (Hall, 2006). The purpose of this study was to shed light on this controversy through an exploration of unhealthy and healthy perfectionist athletes' experiences with, and responses to, competitive success and failure (see Flett & Hewitt, 2005). From an initial pool of 122 intercollegiate athletes (M age = 21.13 years, SD = 2.42), seven unhealthy perfectionists (5 females, 2 males) and four male healthy perfectionists were purposefully sampled based on their Sport-MPS-2 (Gotwals & Dunn, 2009) score profiles to participate in semi-structured interviews. Inductive content analysis produced themes regarding definitions of success/failure, perceived factors that contributed towards success/failure, and responses to success/failure. Themes were then analyzed to examine unhealthy and healthy perfectionists' responses. Both groups defined success by overcoming difficulties and team successes. Several unhealthy perfectionists also defined success as performing better than others and receiving social validation, and healthy perfectionists stressed the importance of success through hard work. Both groups defined failures as poor personal performances and losing important competitions. Unhealthy perfectionists, but not healthy perfectionists, tended to indicate the negative and lasting impact of failures. Results are discussed in relation to perfectionism theory.Acknowledgments: This study was supported by a grants awarded by Lakehead UniversityOCOs Research Office and Regional Research Committee.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".