Self-Compassion May Have Benefits for Body Image among Women with a Higher Body Mass Index and Internalized Weight Bias
Bibliographic record
Abstract
Negative attitudes towards one’s own body are common among women and are linked to adverse consequences including negative affect, low self-esteem, and eating pathology. Self-compassion has been found effective in improving body image; however, few published studies have examined self-compassion in populations with higher BMIs despite the positive correlation between weight and body dissatisfaction. The current study examined the efficacy of a self-compassion letter-writing exercise versus two active control groups in response to a negative body image induction. The sample of college-aged females (M age = 20.91 years; SD = 5.47) was split between higher and lower BMI to determine whether self-compassion affects body image, affect, and self-esteem differently across weight groups. Weight bias internalization (WBI: i.e., internalization of society’s negative stigma against those with higher BMIs) was examined as a moderator of this relationship in the higher BMI group. Results suggest that letter writing improved body image regardless of condition (p < 0.001). The self-compassion exercise promoted more adaptive body image (p = 0.007) and self-compassion (p = 0.013) than one control condition for those with high WBI. Results suggest that self-compassion can be helpful in ameliorating negative body image for females of all sizes, and that levels of WBI may alter the effect of body image interventions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".