Body-related emotions and depression in breast cancer survivors: Does being inactive or overweight matter?
Bibliographic record
Abstract
Depression is common among breast cancer survivors (BCS) and amplified by negative body-related emotions, such as social physique anxiety (SPA), shame, and guilt. This association may be stronger for women who are overweight and/or inactive.This study examined associations between body-related emotions and depressive symptoms among BCS and explored differences for physical activity (PA) and weight status. BCS (n=169; Mage= 55 yrs) provided data on shame, guilt, SPA, PA (accelerometer), and depression symptoms. Many (51%) BCS were overweight, and 29% met PA guidelines of 150 or more weekly minutes of moderate-to-vigorous PA. Based on Pearson correlation and Fisher z coefficients, the relationships among depression and shame (r=.14 & .45, z=-2.31), guilt (r=.03 & .43, z=-2.92), and anxiety (r=.13 & .43, z=-2.24) were significantly different for healthy versus overweight BCS. Controlling for weight status, the associations for depression to shame (r=.28 & .39, z=-.67), guilt (r=.22 & .23, z=.06), and anxiety(r=.31 & .21, z=.58) were not significantly different for PA groups. These findings suggest that shame and SPA are highly related, offer contextual evidence for shame and guilt having distinct effects on depressive symptoms, and suggest overweight women who report more negative body-related emotions may be most at-risk for depression.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".