The performance trajectory of physical education students differs across subtypes of perfectionism: A piecewise growth curve model of the 2 × 2 model of perfectionism.
Bibliographic record
Abstract
In their 2 × 2 model of perfectionism, Gaudreau and Thompson (2010) proposed that 4 prototypical subtypes of perfectionism—nonperfectionism, pure self-oriented perfectionism, pure socially prescribed perfectionism, and mixed perfectionism—are distinctively associated with antecedents, processes, and outcomes. The purpose of this study was to test whether the 4 subtypes of perfectionism would be associated with different performance trajectories for students who are learning a new motor task across 6 physical education classes. A sample of 97 adolescent students completed a questionnaire measuring perfectionism and were then introduced to acrobatic gymnastics by their physical education teacher. Participants had to practice the movements of acrobatic gymnastics in each class and were evaluated by their physical education teacher at the end of each class. The results of a piecewise multilevel growth model showed a decrease in performance in the first segment (i.e., Classes 1–3) followed by an increase in performance in the second segment (i.e., Classes 3–6). Perfectionism predicted the first segment of the performance trajectory. Overall, the results revealed that pure self-oriented perfectionism was associated with a better performance trajectory than nonperfectionism (Hypothesis 1a) and mixed perfectionism (Hypothesis 4). Conversely, pure socially prescribed perfectionism was associated with a worse performance trajectory than nonperfectionism (Hypothesis 2) and mixed perfectionism (Hypothesis 3). This study offered support for the 4 hypotheses of the 2 × 2 model and has important implications for future research on perfectionism and performance trajectories across different learning environments. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".