University Students’ Perfectionistic Profiles: Do They Predict Achievement Goal Orientations and Coping Strategies?
Bibliographic record
Abstract
The present study aimed at investigating the effect of different perfectionistic latent profiles on university students’ personal goal orientation and coping strategies. Four hundred thirty nine university students (82.5% females) from various departments (38.5% freshmen) participated in the study. Students were asked to complete anonymously three self-report questionnaires in groups in their university classes: (a) the Almost Perfect Scale-Revised was used for measuring perfectionism as a multidimensional construct, (b) the Personal Achievement Goals questionnaire for measuring achievement goal orientation (mastery orientation, performance-approach orientation, and performance-avoidance orientation), and (c) the R-COPE questionnaire for measuring adaptive and maladaptive coping strategies for everyday problems. Latent class analysis was conducted in order to create categorical perfectionistic profiles. The data support the three-group model of adaptive and maladaptive perfectionists and non-perfectionists. The adaptive and maladaptive perfectionistic profiles differ in the level of discrepancy between personal standards and accomplishments and significantly predicted adaptive and maladaptive achievement motivation and coping, respectively.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".