Failure Sensitivity in Perfectionism and Procrastination: Fear of Failure and Overgeneralization of Failure as Mediators of Traits and Cognitions
Bibliographic record
Abstract
The current study investigated perfectionism and procrastination from the trait and cognitive perspectives and addressed how they relate to components of a personal orientation toward failure. A sample of 327 undergraduate students completed three perfectionism measures (i.e., Frost Multidimensional Perfectionism Scale, Hewitt–Flett Multidimensional Perfectionism Scale, and Perfectionism Cognitions Inventory), two procrastination measures (i.e., Lay Procrastination Scale and Procrastinatory Cognitions Inventory), and measures of fear of failure and overgeneralizing failure. Correlational analyses showed that the composite trait dimension of perfectionistic concerns and the cognitive dimension of perfectionistic automatic thoughts had modest links with trait procrastination but much stronger links with the cognitive measure of procrastinatory automatic thoughts. All perfectionism and procrastination measures were significantly correlated with fear of failure and overgeneralization of failure. More extensive analyses showed that fear of failure mediated trait and cognitive pathways between perfectionism and procrastination, and the overgeneralization of failure mediated most pathways. Other evidence supported a sequential mediation between perfectionism and procrastination (i.e., fear of failure followed by the overgeneralization of failure). Overall, the results suggest that procrastinating perfectionists have a cognitive hypersensitivity to failure and a potentially debilitating form of perfectionistic reactivity characterized by overgeneralizing failures to the self. The theoretical and practical implications are discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".