Body image disturbances and disordered eating during pregnancy: a comparison of pregnant women with low and high risk of eating disorders
Bibliographic record
Abstract
The main aims of this study are to examine changes in body image disturbances and disordered eating during pregnancy, among women who presented with low and high risk of eating disorders and to identify predisposing factors (i.e., conditions present in the preconception period) of higher body image disturbances and disordered eating during the first trimester. Two independent samples (n1 = 350, n2 = 179) of pregnant women were included in the study and completed an online survey, including a new body image and disordered eating measure tailored for pregnant women. The first sample contributed to establishing cut-off values for that new measure, which identified women with low and high risk of eating disorders. The second sample was recruited using a longitudinal design with three-time points (T1 = first trimester; T2 = second trimester; T3 = third trimester). Profile analyses revealed that the low-risk group of eating disorders displayed stable body image disturbances and disordered eating during pregnancy, while the high-risk group of eating disorders exhibited decreased body image disturbances and disordered eating from early to mid-pregnancy. Binary logistic regressions revealed that only psychological variables (i.e., history of eating disorders and depression) were significant predictors of high body image disturbances and disordered eating at T1. Our findings emphasize the need to raise awareness within the field of prenatal care to monitor eating disorder symptomatology as well as history of depression and eating disorders in non-clinical women.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".