Psychometric properties of the Weight Self-Stigma Questionnaire (WSSQ) among a sample of overweight/obese French-speaking adolescents [accepted manuscript]
Bibliographic record
Abstract
Purpose: The Weight Self-Stigma Questionnaire (WSSQ) was recently developed to assess the internalization of weight stigma among English-speaking overweight and obese adults. The objective of the present study was to develop and examine the psychometric properties of a French version of the WSSQ, as well as its applicability to adolescents. Methods: The sample comprised 156 overweight and obese adolescents (81 boys, 75 girls, Mage = 16.31). The factor validity and the convergent validity of the French version of the WSSQ were examined using a confirmatory factor analysis and a structural equation model, respectively. Results: The a priori two-factor structure of the WSSQ and the composite reliability of its subscales (self-devaluation and fear of enacted stigma) were supported. Convergent validity analyses revealed that both WSSQ subscales were significantly and (a) negatively correlated with measures of self-esteem and physical appearance, and (b) positively correlated with measures of anxiety, depression, fear of negative appearance evaluation, and eating-related pathology (fear of getting fat, eating-related control, food preoccupation, vomiting-purging behaviors, and eating-related guilt subscales). However, no significant relation was found between the WSSQ subscales and body mass index. Conclusion: These results suggest that the French version of the WSSQ has acceptable psychometric properties and can be used to assess weight self-stigma among overweight and obese adolescents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".