Sleep Quality Moderates the Impact of Place-Based Social Adversity on Physical Health in Women with Breast Cancer Transitioning from Active Treatment to Survivorship
Bibliographic record
Abstract
Social adversity is linked to poorer physical health in breast cancer survivors, highlighting the urgency of addressing health equity. Simultaneously, identifying individual-level factors that mitigate these effects may provide more immediate relief for survivors. This study examined whether four modifiable psychosocial factors—emotion dysregulation, physical activity, sleep disturbance, and social support—moderate the relationship between place-based social adversity and physical health in 255 breast cancer survivors (Mage = 56.03, 74.5% non-Hispanic White) within six months post-treatment. Linear regression analyses with 5000 bootstrapped estimates revealed that sleep disturbance significantly moderated the relationship between place-based social adversity and physical health (B = −0.014, SE = 0.001, bootstrapped 95% CI = −0.027, −0.001). Specifically, greater place-based social adversity was associated with poorer physical health at high levels of sleep disturbance (B = −0.22, p = 0.004), but not at low (B = 0.01, p = 0.94) or average (B = −0.10, p = 0.07) levels. Emotion dysregulation, physical activity, and social support did not moderate this relationship. Findings suggest that improving sleep quality may buffer the negative impact of social adversity on physical health, identifying sleep as a potential target for interventions aimed at reducing disparities among breast cancer survivors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".