Maladaptive Perfectionism and Psychological Distress: The Mediating Role of Resilience and Trait Emotional Intelligence
Bibliographic record
Abstract
University students experience significantly high levels of psychological distress. Maladaptive perfectionism has been identified as a common trait among students that leads to diagnosed conditions such as depression and anxiety. Resilience and trait emotional intelligence have also been identified as common predictors of psychological illness and mediators between related maladaptive perfectionism. However, no current research has investigated maladaptive perfectionism’s relationship with a more general psychological distress experienced by university students. Therefore, the current study aimed to investigate maladaptive perfectionism, resilience and trait emotional intelligence association with psychological distress in 171 university students (29 males; 138 females; Mage = 28.48 years; SD = 11.58). Results identified maladaptive perfectionism to significantly, positively correlate with psychological distress in university students. The combination of increased maladaptive perfectionism, low resilience and low trait emotional intelligence significantly predicted psychological distress. Additionally, resilience and trait emotional intelligence significantly added to the prediction of psychological distress, above and beyond maladaptive perfectionism. Finally, resilience and trait emotional intelligence both partially mediated the relationship between maladaptive perfectionism and psychological distress in university students. Findings suggest resilience and trait emotional intelligence to be important factors in predicting general psychological distress in student maladaptive perfectionists. The current study provided additional supporting evidence for the importance of resilience and trait emotional intelligence in intervention and prevention strategies for psychological distress in maladaptive perfectionist students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".