Development and Validation of a Short Form of the German Self-Consciousness Scale for the General Population
Bibliographic record
Abstract
Abstract: Background: The German translation of the Self-Consciousness Scale ( Fragebogen zur Erfassung dispositionaler Selbstaufmerksamkeit [SAM]) is a 27-item, self-report measure assessing self-consciousness. Previous studies showed a poor fit for the proposed two-factor structure, assessing private and public self-consciousness. We assessed the factor structure of the German version to develop and validate a short form, aiming to improve model fit and provide a brief version for use in research and clinical practice. Method: Participants were 2,326 adults representative of the German general population, divided randomly into two comparable samples: Sample A and Sample B. We used confirmatory factor analysis (CFA) with Sample A to inform item selection; we used Sample B to evaluate the proposed short form, examining structural validity, measurement invariance, and convergent validity with measures assessing quality of life, well-being, neuroticism, and stress. Results: The results support a two-factor hierarchical model for the 12-item short form, demonstrating full measurement invariance across gender, age, and education level. We found positive correlations between self-consciousness, quality of life, well-being, and chronic stress. Discussion: The results support the use of the SAM-SF to provide a total score as well as subscale scores for private and public self-consciousness.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".