Self-objectification and eating disorder pathology in an ethnically diverse sample of adult women: cross-sectional and short-term longitudinal associations
Bibliographic record
Abstract
BACKGROUND: Extensive support exists for objectification theory's original aim of explaining patterns of women's mental health risk through a sociocultural lens. One pathway in objectification theory proposes a mediational role of body shame in the relationship between self-objectification and eating disorder (ED) pathology. Robust past cross-sectional research supports this proposed pathway, but largely in non-Hispanic Caucasian, college-aged samples; this pathway has yet to be empirically demonstrated longitudinally. Given previously documented concerns regarding direct measurement of body shame, we tested two measures of body shame as mediators in both cross-sectional and longitudinal models in a diverse sample of adult women. METHOD: Utilizing snowball sampling via email, we recruited age and racially/ethnically diverse women predominantly within the United States. Participants completed online surveys assessing self-objectification (operationalized as body surveillance), body shame, and ED pathology at baseline, 3-months and 6-months. RESULTS: = 181) adult women completed the measures. Cross-sectional moderated mediation models indicated that racial/ethnic status did not moderate relationships, and that body shame significantly mediated the relation between body surveillance and ED pathology at each time point. The longitudinal model, analyzed using cross-lagged panel analyses, was nonsignificant, as body surveillance failed to predict future body shame when controlling for past body shame. CONCLUSIONS: Racial/ethnic status did not moderate relations at any time point. Cross-sectional findings replicated past research; the longitudinal model did not support a core mediation pathway linking self-objectification to ED pathology through body shame. Because self-objectification putatively develops earlier in life, future research also should examine these relations in younger diverse samples over a longer time period.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".