Arabic Translation of the Weight Self-Stigma Questionnaire: Instrument Validation Study of Factor Structure and Reliability
Bibliographic record
Abstract
BACKGROUND: While it is most often associated with its effects on physical health, obesity is also associated with serious self-stigmatization. The lack of a suitable, validated tool to measure weight-related self-stigma in Arabic countries may be partly responsible for the scarcity of literature about this problem. OBJECTIVE: This study investigated the reliability and validity of an Arabic version of the Weight Self-Stigma Questionnaire (WSSQ). METHODS: Data on the Arabic-translated version of the 12-item WSSQ were collected using two cross-sectional electronic questionnaires distributed among Saudi nationals through the Sharik Association for Health Research's database in June 2020. Internal consistency, test-retest reliability, and exploratory factor analysis of the Arabic WSSQ were assessed and compared with the original English version and other translations. RESULTS: For reliability analysis, 43 participants completed the Arabic WSSQ during two time periods. Internal consistency was α=.898 for the overall survey, α=.819 for the fear of enacted stigma subscale (factor 1), and α=.847 for the self-devaluation subscale (factor 2). The test-retest reliability of the intraclass correlation coefficient was α=.982. In the factor structure analysis, 295 participants completed the questionnaire. The Arabic WSSQ loading of the items was consistent with the original WSSQ, except for the loading of item 9, which was stronger in factor 2 than in factor 1. The two factors accounted for the observed variances of 47.7% and 10.6%. CONCLUSIONS: The Arabic version of the WSSQ has good internal consistency and reliability, and the factorial structure is similar to that of the original WSSQ. The Arabic WSSQ is adaptable for clinicians seeking to assess weight-related self-stigma in Arabic-speaking people.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".