The relationship between personality traits and perfectionistic tendencies in female athletes
Bibliographic record
Abstract
There is a vast body of research that describes the potential challenges of sport engagement, such as the development of unhealthy levels of perfectionism in some athletes (Crocker, 2010). Perfectionism is a multifaceted construct that is based on individuals setting unrealistic and unattainable standards (Martin & Swinson, 2009). High levels of perfectionism are associated with lowered self-esteem, increased body dissatisfaction, and disordered eating behaviours (Flett & Hewitt, 2005; Nepon et al., 2011). The purpose of this study was to examine the relationship between personality traits and perfectionism in a female athlete sample to understand better what characteristics were more likely to be associated with perfectionistic tendencies (N =31; Mage = 21.4 years; SD = 1.7 years). Athletes were members of team sports (i.e., volleyball, basketball, soccer/futsal, & hockey), and were competing at ACAL, ACAC, and CIS Universities and Colleges in Alberta, Canada. The participants completed a descriptive survey, the Keirsey Temperament Sorter (Keirsey, 1998), and the Perfectionism Inventory (Hill et al., 2004). Results showed a significant positive correlation between the personality trait introversion and perfectionism (r = 0.55 p < .05). These results suggested that as introversion increases levels of perfectionism also increase for female athletes. These findings are important in the area of sport psychology because perfectionism can be debilitating for athletes; understanding more about how personality relates to perfectionism is the first step toward better managing perfectionism in sport. Further research should explore how female athletes manage challenges such as perfectionism in sport.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".