Moderation by weight status of the associations between positive and negative weight commentary and body image-related indicators in young adults
Bibliographic record
Abstract
OBJECTIVES: To assess whether associations between positive or negative weight commentary and body-related emotions, internalized weight bias, and weight worry differ by weight status among young adult males and females. METHODS: Participants were from the Nicotine Dependence in Teens study, initiated in 1999-2000. For this cross-sectional analysis, self-report data collected online in 2023 were available for 687 young adults (57% female; Mean age = 35.3 years). Sex-stratified analyses compared mean scores for eight body image-related indicators by frequent positive or negative weight commentary (yes/no) and weight status (lower weight vs. higher weight). Moderation was tested using product terms in multivariable linear regression. RESULTS: Among females, 44% reported frequent positive commentary (47% lower weight; 41% higher weight) and 13% reported frequent negative commentary (10% lower weight; 16% higher weight). Positive commentary was associated with lower shame, guilt, embarrassment, and internalized weight bias, with stronger protective effects among females with higher weight. Negative commentary was associated with greater body-related distress and weight-related worry, also with stronger effects among females with higher weight. Among males, positive and negative commentary showed modest associations with body image-related indicators, and there was little evidence that weight status modified these associations. CONCLUSIONS: Associations between weight commentary and body image-related indicators were moderated by weight status in females but not in males. For women with higher weight, positive remarks were somewhat protective, while negative remarks appeared to have disproportionately adverse effects. Findings suggest the need for weight-neutral, sensitive approaches to weight discussions in clinical and social settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".