Development of a visual‐perceptual method to assess body image: A cross‐cultural validation in Canadian and Spanish women
Bibliographic record
Abstract
The objectives of this study were (a) to explore the preliminary cross-cultural validity of a visual-perceptual method to assess body image; (b) to examine potential differences and similarities in body image phenomena between women from two Western countries (i.e., Canada and Spain). 201 self-identified women participated in this cross-sectional study. Ideal, normal, and self-perceived body sizes were assessed using a visual-perceptual method, whereas body dissatisfaction was measured using both a visual-perceptual method and a questionnaire. Visual-perceptual body dissatisfaction was significantly correlated with questionnaire body dissatisfaction, suggesting a preliminary convergent validity between the two assessment methods. Women in both countries were dissatisfied with their bodies. Compared to their self-perceived body, all women chose a significantly thinner visual representation of their "normal" and ideal body. These results may suggest a shift towards the "thin" body as not only ideal, but also normative. This study provides the first evidence for the cross-cultural validity of a visual-perceptual body image assessment tool. The results of the current study confirm the presence of "normative discontent", and suggest more cross-country similarities than differences among women from these two Western societies.
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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 | 0.009 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".