Going beyond appearance: Embodiment throughout adolescence and its associations with identity functioning and eating behaviors
Bibliographic record
Abstract
The complex relationship between body (dis)satisfaction, identity, and disordered eating in adolescence has been highlighted in previous studies. However, longitudinal research on this interplay using a holistic approach in assessing embodiment is lacking. The current study used three-wave longitudinal data (T1: N = 923; 59.6 % female; M age = 16.19, SD = 1.31, range = 13–21 years) to examine the development of embodiment during adolescence and to explore its (temporal) associations with identity, eating disorder symptoms, and eating competence. First, cross-lagged analyses revealed that embodiment positively predicted identity synthesis and eating competence skills over time, whereas it negatively predicted identity confusion and eating disorder symptoms over time, with some associations being bidirectional. Second, latent growth curve modeling indicated that embodiment showed increases throughout adolescence and emerging adulthood. Relatedly, interindividual differences in development were demonstrated, with latent class growth analysis pointing to four embodiment trajectory classes (high, moderate, low, and problematic embodiment). Finally, multigroup latent growth curve modeling suggested that the embodiment trajectory classes differed meaningfully on identity functioning, eating disorder symptoms, and eating competence skills. Overall, this study suggests the importance of embodiment for both research and clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".