Exploring the reciprocal relationships between body image flexibility and body fat and muscularity dissatisfaction: An 18-month longitudinal study in Chinese adolescents
Bibliographic record
Abstract
Body image flexibility has shown robust negative associations with body dissatisfaction. However, research in this area is confined to cross-sectional studies on adults in Western cultural contexts. Responding to these gaps and the unique cultural nuances and increasing prevalence estimates of body dissatisfaction in China, we examined the bi-directional nature of body image flexibility and body fat and muscularity dissatisfaction in Chinese adolescent boys and girls (N = 1381, 57.3 % girls) at two points over 18 months (Wave [W] 1=baseline, W2=18 months later). We also explored sex differences in longitudinal models. In boys, higher W1 body image flexibility was associated with lower W2 body fat dissatisfaction, and higher W1 body fat dissatisfaction was associated with lower W2 body image flexibility. Null prospective associations between body image flexibility and muscularity dissatisfaction were identified in boys. In girls, higher W1 body fat and muscularity body dissatisfaction were associated with lower W2 body image flexibility. Higher W1 body image flexibility was associated with lower W2 body fat and muscularity dissatisfaction in girls. We found no significant sex differences in the models. Findings advance a multicultural understanding of the temporal and bi-directional links between body image flexibility and body fat and muscularity dissatisfaction in Chinese adolescents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".