Predicting competition contingent self-worth in university varsity athletes. Does gender moderate the effects of self-compassion and sport perfectionism?
Bibliographic record
Abstract
This research examines if gender moderates the effects of sport perfectionism and self-compassion (SC) on contingent self-worth (CSW) in university athletes. Research suggests perfectionism and self-compassion are related to contingencies of self-worth (Hill et al., 2011; Neff & Vonk, 2009). Participants were 262 varsity athletes (NFemale=126) from British Columbia and Quebec who completed measures of SC (Self-Compassion Scale; Neff, 2003), sport perfectionism [Sport Multidimensional Perfectionism Scale-2 (Sport-MPS-2); Gotwals & Dunn, 2009], and a sport-adapted measure of competitive CSW (J. Crocker, 2003). First, mean gender differences were examined, with multivariate analysis revealing gender differences in SC (p = .04, η2 = .017) and the parental pressure subscale of the Sport-MPS-2 (p = .03, η2 = .019), with males scoring higher on both. Next, a moderated regression analysis examined if gender moderated the effects of SC and perfectionism in predicting CSW. SC, Sport-MPS-2, and gender were entered in Step 1, with interaction terms entered in Step 2. Although SC was a significant correlate of CSW (r = -.25, p .18). Further research is needed to clarify the independent and joint effects of perfectionism and SC on CSW and the subsequent impact of CSW on athletic adjustment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".