One size does not fit all: Trajectories of body image development and their predictors in early adolescence
Bibliographic record
Abstract
Background: Negative body image predicts many adverse outcomes. The current study prospectively examined patterns of body esteem development in early adolescence and identified predictors of developmental subtypes. Methods: 328 girls and 429 boys reported annually across a 4-year period (Mage at baseline = 11.14, SD = 0.35) on body esteem, appearance ideal internalization, perceived sociocultural pressures, appearance comparisons, appearance-related teasing, self-esteem, positive and negative affect, and dietary restraint. We performed latent class growth analyses to identify the most common trajectories of body esteem development and examine risk and protective factors for body image development. Results: Three developmental subgroups were identified: (a) high body esteem (39.1%); (b) moderate body esteem (46.1%); and (c) low body esteem (14.8%). Body esteem was stable within the low trajectory and there were minor fluctuations in the high and moderate trajectories. Greater appearance-related teasing, lower self-esteem, less positive affect, and higher dietary restraint predicted the low trajectory, whereas higher self-esteem and lower dietary restraint best predicted the high trajectory. Conclusions: Low body esteem appears to be largely stable from age 11 years. Prevention programming may be enhanced by incorporating components to address transdiagnostic resilience factors such as self-esteem and positive affect.
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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 | 0.004 |
| 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".