Psychometric properties of the French version of the social anxiety questionnaire for adults
Bibliographic record
Abstract
Background: The Social Anxiety Questionnaire for Adults (SAQ) is a new social anxiety measure that attracts attention for its empirical development, validation with large samples and in multicultural contexts. The SAQ has shown adequate psychometric properties among clinical and non-clinical samples, from 20 different countries, including Spain, Portugal and most Latin American countries. To date however, this questionnaire has not been translated or validated in French. Aims: The aim of this study is to present the French version of the SAQ and analyze its psychometric properties in French Canadian and Belgian samples. Method: The original version of the SAQ was translated into French. A total of 482 Canadian and Belgian non-clinical participants were recruited for this study. All participants were administered the French versions of the SAQ and the Liebowitz Social Anxiety Scale (LSAS-SR). Results: Confirmatory factor analyses indicated an adequate fit of the five-factor model. The internal consistency was excellent for the total score and very good for all dimensions, and the test-retest reliability was good for both the total score and all dimensions (over a 6-week period). An adequate convergent validity of the SAQ with the LSAS-SR was found. Differences between countries and sexes in the SAQ were also examined, and small to medium effect sizes were noted in some scores. Conclusions: The French version of the Social Anxiety Questionnaire for Adults (SAQ) demonstrated adequate reliability and validity in the evaluated samples.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".