The Self-Generated Stress Scale: Development, Psychometric Features, and Associations With Perfectionism, Self-Criticism, and Distress
Bibliographic record
Abstract
In the current article, we describe the development and validation of a self-report measure of self-generated stress and its associations with measures of perfectionism, self-criticism, and distress. The Self-Generated Stress Scale is a seven-item inventory that taps the tendency to see oneself as someone who generates and adds to existing personal stress (i.e., making a challenging situation worse or turning a life problem into a bigger problem). Psychometric analyses with data from three samples of university students showed that the Self-Generated Stress Scale has one factor and acceptable internal consistency. Analyses established that self-generated stress is associated with trait perfectionism, self-criticism, dependency, and self-silencing, as well as indices tapping cognitive perfectionism and perfectionistic self-presentation. Self-generated stress was also associated with distress and psychosomatic health symptoms. Regression analyses established that scores on the Self-Generated Stress Scale predict unique variance in distress and negative affect beyond the variance attributable to self-criticism and other measures of stress. Overall, our findings attest to the further use of the Self-Generated Stress Scale in various contexts and highlight that certain perfectionists suffer from a perceived tendency to make their lives more stressful. The implications of these findings are discussed along with directions 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".