Perfectionism and Stress as Predictors of Academic Self-Efficacy, Self-Concept, and Burnout: A Test of the Vulnerability Stress Model
Bibliographic record
Abstract
Introduction: Self-critical perfectionism and stress have been implicated as risk factors for maladaptive academic outcomes (i.e., decreased academic self-efficacy, decreased academic self-concept, and increased academic burnout). The present study investigated a vulnerability-stress model, testing whether perfectionism (self-critical and rigid) moderates the relationship between stressor severity (academic and interpersonal) and academic outcomes. Method: A sample of 384 post-secondary students (76.8% women, average age: 20.06) completed a cross-sectional survey involving questionnaires assessing the constructs of interest. Results: Stressor severity (both academic and interpersonal) predicted decreased academic self-efficacy and academic self-concept, as well as increased academic burnout, when controlling for perfectionism. Self-critical perfectionism predicted decreased academic self-efficacy and increased academic burnout, when controlling for stressor severity. Main effects of rigid perfectionism generally predicted increased academic self-efficacy and increased academic self-concept when controlling for stressor severity. None of the proposed interaction effects were statistically significant, failing to support a vulnerability-stress model. Discussion: Findings suggest that stressor severity (both academic and interpersonal) and self-critical perfectionism are strong predictors of maladaptive academic outcomes and may serve as risk factors for poor academic functioning. The lack of an interaction effect suggests that a vulnerability-stress model may not explain why perfectionism leads to maladaptive academic outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".