The Social Comparison Rumination Scale: Development, Psychometric Properties, and Associations With Perfectionism, Narcissism, Burnout, and Distress
Bibliographic record
Abstract
In the current article, we describe the development and validation of the Social Comparison Rumination Scale. This measured was developed as a supplement to existing social comparison measures and to enable us to determine its potential relevance to perfectionism and other personality constructs. The Social Comparison Rumination Scale (SCRS) is a six-item inventory assessing the extent to which an individual is cognitively preoccupied and thinking repetitively about social comparison outcomes and information. Three studies with five samples of university students are described. Psychometric analyses established the SCRS consists of one factor assessed with high internal consistency and the measure is reliable and valid. Analyses showed that elevated levels of social comparison rumination are associated with trait perfectionism, perfectionistic automatic thoughts, perfectionistic self-presentation, ruminative brooding, burnout, depression, and fear of negative evaluation. Links were also established between social comparison rumination and both narcissism and dispositional envy. Overall, our findings support the further use of the SCRS and highlight the tendency of many people to think in deleterious ways about social comparisons long after the actual comparisons have taken place. We discuss social comparison rumination within the context of concerns about excessive social media use and young people being exposed to seemingly perfect lives that became a vexing cognitive preoccupation.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 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".