Perceived stigmatization and social comfort: Validating the constructs and their measurement among pediatric burn survivors.
Bibliographic record
Abstract
OBJECTIVE: The current study implemented a four-step process to evaluate the measurement properties of the Perceived Stigmatization Questionnaire (PSQ) and the Social Comfort Questionnaire (SCQ) among long-term pediatric burn survivors. METHODS: First, a series of confirmatory factor analyses (CFAs) compared the hypothesized four-factor model--3 perceived stigmatization factors (absence of friendly behavior, confused and staring behavior, and hostile behavior)--and one social comfort factor to three other models. Second, we tested the measurement invariance of the instruments between pediatric and adult burn survivor samples. Third, possible differences in structural parameters across groups were tested. Fourth, we tested whether the three perceived stigmatization factors and the social comfort factor loaded on one second-order factor. Participants included 369 pediatric and 347 adult burn survivors. RESULTS: The four-factor model was superior to the comparison models. The PSQ and SCQ demonstrated measurement invariance. Factor variance, factor covariance, and the latent means of the PSQ did not vary across groups. The adult group had a significantly lower latent mean on the SCQ than the pediatric group. The three factors of the PSQ and the one-factor SCQ loaded on one second-order factor. CONCLUSION: The results of this study lend support to both the construct validity of perceived stigmatization and social comfort and the potential value of the PSQ and SCQ for studying the social experience of people with visible differences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".