Emotional consequences of self-weighing: A daily diary study in women with comorbid history of breast cancer and obesity
Bibliographic record
Abstract
Background. Breast cancer is often comorbid with overweight and obesity, with approximately 65% of women receiving a cancer diagnosis having a body mass index classified as overweight or obese. Since overweight status can worsen cancer outcomes and survival, a behavioral intervention of self-monitoring weight is commonly recommended in clinical practice. Purpose. However, given emerging evidence that self-weighing may have psychological consequences, the present pilot study aims to examine the effects of daily self-weighing on self-conscious emotions of guilt and shame. Methods. Women (n = 52) with a history of breast cancer who are seeking to manage their weight completed a weeklong daily diary study, where they self- weighed every morning and reported emotions associated with their weight both acutely (i.e., immediately after self-weighing) and distally (i.e., cumulative throughout day). Results. Women reported higher acute and distal shame and guilt during days in which their weight was higher than average. A history of weight cycling pre- and post-diagnosis did not moderate the daily relationship between weight and negative emotions. Conclusions. Drawing on these preliminary findings, we can conclude that recommended practices around frequent self-weighing may be psychologically detrimental among this vulnerable subset of women with obesity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".