Perfectionistic self-presentation, coping, and affective response during laboratory performance
Bibliographic record
Abstract
Background and Objectives The impact of trait perfectionism on coping and affective response has been well-documented in laboratory performances, and yet little is known about the role played by perfectionistic self-presentation in performances beyond the effects of trait perfectionism.Design We aimed to address this knowledge gap by examining the relationships between perfectionistic self-presentation, maladaptive emotion-focused coping, and affective response to laboratory problem-solving tasks.Methods A group of 130 undergraduates attempted challenging, time-limited arithmetic and anagram tasks. Upon task completion, participants’ scores were announced along with their mistakes. Additionally, participants completed measures assessing their positive and negative affect before and after lab performance, as well as coping strategies utilized during performance.Results Participants with elevated perfectionistic self-presentation experienced greater levels of negative affect and maladaptive emotion-focused coping. Moreover, nondisclosure of imperfection emerged as a significant predictor of lowered positive affect from pre- to post-performance after controlling for self-oriented perfectionism and socially prescribed perfectionism. Path analysis indicated that both nondisplay of imperfection and nondisclosure of imperfection exerted an indirect effect on post-performance negative affect via maladaptive emotion-focused coping.Conclusions Our findings highlight the unique contribution of perfectionistic self-presentation beyond trait perfectionism in performance settings and suggest a need for future research.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".