Distinguishing perfectionism and excellencism in graduate students: Contrasting links with performance satisfaction, research self‐efficacy, burnout, and dropout intentions
Bibliographic record
Abstract
Research on perfectionism in graduate school found inconsistent associations between perfectionistic standards and psychological outcomes. Such unanticipated results led to the Model of Excellencism and Perfectionism (MEP), which differentiates between people pursuing excellence and those pursuing perfection. Recent studies with undergraduate students have shown that excellencism and perfectionism are distinct constructs, differentially associated with achievement and psychological outcomes. In this study, we aimed to offer the first empirical test of the MEP in graduate school with a sample of 376 graduate students (i.e. 81% masters, 19% doctoral). Results of confirmatory factor analyses on the Scale of Perfectionism and Excellencism provided evidence for the conceptual separation of excellencism and perfectionism. Results of multiple regression showed that perfection strivers (compared to excellence strivers) reported higher research self-efficacy and satisfaction with their research productivity. When accounting for satisfaction with research productivity, perfection strivers experienced more academic burnout and dropout intentions. Perfectionistic students also used more perfectionistic self-presentation strategies when interacting with their supervisors. Perfectionism was associated with both beneficial and harmful outcomes, which suggests that perfectionism in graduate school is paradoxical and operates like a double-edged sword. These findings are interpreted in light of the need to help graduate students strike a balance between their academic achievements and psychological 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".