A conditional process model of perfectionism, goal-realization, and post-competition mood
Bibliographic record
Abstract
Research has recently begun to examine the relationship between multidimensional perfectionism and athletes' post-competition mood. However, to date, there have been few attempts to examine the interaction between dimensions of perfectionism or model possible explanatory processes. To address these limitations, in the current study we tested a novel conditional process model whereby the relationship between perfectionistic strivings and post-competition affect was mediated by the degree to which goals were considered to have been met (goal-realization) and that this indirect effect was, in turn, moderated by levels of perfectionistic concerns. We tested this model in a sample of 251 athletes who took part in a "Runmageddon" event - a cross-country obstacle race. Athletes completed measures of perfectionism (perfectionistic strivings and perfectionistic concerns) before the race and measures of goal-realization and mood (tense arousal, energetic arousal, and hedonic tone) between 24 and 48 h after the race. Analyses revealed that perfectionistic strivings were indirectly linked to a more unpleasant post-competition mood (higher tense arousal and lower hedonic tone) via perceptions of lower goal-realization. In addition, these two indirect effects were statistically significant only when perfectionistic concerns were medium and high. The results support the proposed conditional model and suggest the interplay between dimensions of perfectionism is important for athletes' post-competition mood, and the level of perfectionistic concerns, especially.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".