Reliability and Validity of Three Versions of the Brief Fear of Negative Evaluation Scale in Patients With Systemic Sclerosis: A Scleroderma Patient‐Centered Intervention Network Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Fear of negative evaluation is a common concern among individuals with visible differences but has received limited attention in systemic sclerosis (SSc), which can involve substantial changes to appearance. The Brief Fear of Negative Evaluation Scale (BFNE) was specifically designed to evaluate fear of negative evaluation. There are currently 3 versions of the BFNE with strong demonstrated measurement properties: two 8-item versions (BFNE-S, BFNE-8) and one 12-item version (BFNE-II). The present study evaluated these versions in SSc, and identified the most appropriate version for use among SSc patients. METHODS: Participants were 1,010 patients with SSc enrolled in the Scleroderma Patient-Centered Intervention Network cohort. Multiple group confirmatory factor analysis, Cronbach's alpha, and Pearson's product-moment correlations were used to evaluate structural validity, internal consistency reliability, and convergent and divergent validity, respectively. RESULTS: Confirmatory factor analysis demonstrated that 1-factor models fit acceptably well for the 12-item BFNE-II, the 8-item BFNE-S, and the 8-item BFNE-8. Additionally, all Cronbach's alphas demonstrated excellent internal consistency reliability (BFNE-II = 0.98, BFNE-S = 0.97, BFNE-8 = 0.96), and all versions had comparable associations with measures of social anxiety, body-related attitudes, depression, age, and education. CONCLUSION: Psychometric support was found for all 3 versions of the BFNE, although the longer 12-item BFNE-II did not improve measurement compared to the shorter 8-item versions (BFNE-S and BFNE-8). Of these 2, the BFNE-S has been more widely studied, with strong validity data in a greater number of populations. Therefore, the BFNE-S is recommended to assess fear of negative evaluation among patients with SSc.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".