Normative body image development: A longitudinal meta-analysis of mean-level change
Bibliographic record
Abstract
This meta-analysis synthesized longitudinal data on mean-level change in body image, focusing on the constructs of body satisfaction and dissatisfaction, body esteem, perceived attractiveness, valuation, self-objectification, and body shame. We searched five databases and accessed unpublished data to identify studies that assessed body image at two or more time points over six months or longer. Analyses were based on data from 142 samples representing a total of 128,254 participants. The age associated with the midpoint of measurement intervals ranged from 6 to 54 years. Multilevel metaregression models examined standardized yearly mean change, and the potential moderators of body image construct, gender, birth cohort, attrition rate, age, and time lag. Boys and men showed fluctuations in overall body image with net-improvements between ages 10 and 24. Girls and women showed worsening body image between ages 10 and 16, but improvements between ages 16 and 24. Change was greatest between ages 10 and 14, and stabilized around age 24. We found no effect of construct, birth cohort, or attrition rate. Results suggest a need to revise understandings of normative body image development: sensitive periods may occur somewhat earlier than previously believed, and body image may show mean-level improvements during certain age ranges.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".