Perfectionism, optimism, and pessimism among intercollegiate female varsity athletes
Bibliographic record
Abstract
This study examined the degree to which perfectionistic strivings and perfectionistic concerns were related to dispositional optimism and pessimism following poor performances in sport among 99 female intercollegiate team-sport athletes (M age = 20.15 years, SD = 1.78). Perfectionistic strivings were measured with a composite score derived from the Personal Standards and Organization subscales of the Sport-MPS-2 (see Gotwals & Dunn, 2009). Perfectionistic concerns were measured with a composite score derived from the Concern Over Mistakes, Perceived Coach Pressure, Perceived Parental Pressure, and Doubts About Actions subscales of the Sport-MPS-2 (see Gotwals & Dunn, 2009). Optimism and pessimism were measured with a sport-modified version of the Life Orientation Test-Revised (Scheier & Carver, 1994). All subscales had acceptable levels of internal consistency (as > .83). Bivariate correlations revealed that perfectionistic strivings were not significantly correlated to optimism or pessimism. In contrast, perfectionistic concerns were negatively correlated with optimism (r = -.32, p < .005) and positively correlated with pessimism (r = .49, p < .005). Results of sequential regression analyses indicated that perfectionistic strivings were positively related to optimism (beta = .20, p < .05) and negatively related to pessimism (beta = .22, p < .05) when the overlap with perfectionistic concerns was controlled. Results provide further support for the adaptive role that heightened perfectionistic strivings can play in sport when the overlap with perfectionistic concerns is controlled (see Gotwals, Stoeber, Dunn, & Stoll, 2012).Acknowledgments: This research was supported by a grant from the Sport Science Association of Alberta.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".