Translation and Validation of a State-Measure of Body Image Satisfaction: The Body Image State Scale
Bibliographic record
Abstract
The aim of the present study is to test the validity and reliability of the French Body Image State Scale (F-BISS). The scale was translated using a back-translation technique, with discrepancies being settled through consensus. Three hundred and twelve female participants were recruited. Convergent validity was assessed using eating disorder evaluation and social comparison. Exploratory and confirmatory factor analyses were also conducted. The translated Body Image State Scale (BISS) demonstrated good psychometric properties, with good internal consistency (α = 0.83), and adequate goodness-of-fit. The translated BISS presented a unifactorial structure, with one factor explaining 56% of the variance. The exploratory factor analysis led to the removal of a single item due to insufficient factor loading (<0.45). Its convergent validity seems consistent with previous literature. Discriminant analyses showed a significant difference in F-BISS score between participants relative to eating disorder symptomatology ( t = 11.65; p < 0.001). This translation could prove useful in both research and clinical settings to assess state body satisfaction in French populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".