The Relationship Between Mindfulness and Perfectionism in University Students During Emerging Adulthood
Bibliographic record
Abstract
The present study examined the empirical and conceptual association between two definitions of trait mindfulness (socio-cognitive and mindfulness-meditation) and trait, cognitive, and self-presentational components of perfectionism. These associations were investigated in a student sample of emerging adults ( n = 390), who completed measures of trait perfectionism, perfectionistic self-presentation, perfectionistic cognitions, trait mindfulness-meditation, and trait socio-cognitive mindfulness. Hierarchical linear regression analyses revealed that socially prescribed perfectionism was negatively related to the non-reactivity and acting with awareness facets of mindfulness-meditation, while self-oriented perfectionism had a positive relationship with the novelty seeking and novelty producing facets of socio-cognitive mindfulness. Perfectionistic cognitions were negatively associated with non-judging. Non-display and non-disclosure of imperfections were also negatively associated with facets of mindfulness-meditation and socio-cognitive mindfulness. Level of study (i.e., undergraduate or graduate) did not moderate the associations between perfectionism and mindfulness variables. Our study provides evidence for negative and positive associations between specific mindfulness facets and components of perfectionism. The results have implications for a theoretical and empirical understanding of the relationship between perfectionism and mindfulness and can inform the design of interventions to teach students how to cope with distressing forms of perfectionism during emerging adulthood.
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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) | 0.001 | 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".