Development and Validation of the Body Concealment Scale for Scleroderma
Bibliographic record
Abstract
OBJECTIVE: Body concealment is a component of social avoidance among people with visible differences from disfiguring conditions, including systemic sclerosis (SSc). The study objective was to develop a measure of body concealment related to avoidance behaviors in SSc. METHODS: Initial items for the Body Concealment Scale for Scleroderma (BCSS) were selected using item analysis in a development sample of 93 American SSc patients. The factor structure of the BCSS was evaluated in 742 Canadian patients with single-factor, 2-factor, and bifactor confirmatory factor analysis models. Convergent and divergent validity were assessed by comparing the BCSS total score with the Brief-Satisfaction with Appearance Scale (Brief-SWAP) and measures of depressive symptoms and pain. RESULTS: A 2-factor model (Comparative Fit Index [CFI] 0.99, Tucker-Lewis Index [TLI] 0.98, Root Mean Square Error of Approximation [RMSEA] 0.08) fit substantially better than a 1-factor model (CFI 0.95, TLI 0.94, RMSEA 0.15) for the 9-item BCSS, but the Concealment with Clothing and Concealment of Hands factors were highly correlated (α = 0.79). The bifactor model (CFI 0.99, TLI 0.99, RMSEA 0.08) also fit well. In the bifactor model, the omega coefficient was high for the general factor (ω = 0.80), but low for the Concealment with Clothing (ω = 0.01) and Concealment of Hands (ω = 0.33) factors. The BCSS total score correlated more strongly with the Brief-SWAP Social Discomfort (r = 0.59) and Dissatisfaction with Appearance (r = 0.53) subscales than with measures of depressive symptoms and pain. CONCLUSION: The BCSS sum score is a valid indicator of body concealment in SSc that extends the concepts of body concealment and avoidance beyond the realms of body shape and weight to concerns of individuals with visible differences from 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 | 0.008 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".