Psychometric Properties of the Body Checking Questionnaire (BCQ) and of the Body Checking Cognitions Scale (BCCS): A Bifactor-Exploratory Structural Equation Modeling Approach
Bibliographic record
Abstract
This research sought to assess the psychometric properties of the French versions of the Body Checking Questionnaire and the Body Checking Cognitions Scale among community samples. A total sample of 922 adolescents and adults was involved in a series of two studies. The results from the first study supported factor validity and reliability of responses obtained on these two measures, and showed that both measures were best represented by a bifactor-exploratory structural equation modeling representation of the data. The results from the second study replicated these conclusions, while also supporting the measurement invariance of the bifactor-exploratory structural equation modeling solution and the equivalence of the correlations among the two measures (i.e., convergent validity) across samples. This second study also supported the criterion-related validity of ratings on both measures with measures of global self-esteem, physical appearance, social physique anxiety, fear of negative appearance evaluation, and disturbed eating attitudes and behaviors. Finally, the results of this last study also supported the measurement invariance and lack of differential item functioning of both measures in relation to sex, age, diagnosis of eating disorders, and body mass index.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".