Changes in social support predict emotional well-being in breast cancer survivors
Bibliographic record
Abstract
BACKGROUND: Breast cancer survivors who have completed surgery and adjuvant treatment have distinct social support needs that may relate to emotional health. There is little research on both levels of social support following treatment and the association between social support and emotional well-being over time following breast cancer diagnosis and treatment. The aims of this study were to assess (1) the direction and magnitude of change in social support quality and quantity and (2) the degree to which change in quality and quantity of social support predicted change in emotional well-being over time following completion of breast cancer treatment. METHODS: = 55, SD = 11 years) completed a baseline and a 1-year follow-up questionnaire assessing sociodemographic information, quality and quantity of social support, and emotional well-being including depression symptoms, stress, and positive and negative affect. RESULTS: Social support quantity significantly decreased over 1 year, while social support quality remained stable. Based on change score analyses, a decrease in social support quality was a significant predictor of increases in depression, stress, and negative affect, explaining an extra 4 to 6% of variance in the emotional well-being outcomes compared with social support quantity. CONCLUSIONS: This study highlights the decline in social support among recently treated female breast cancer survivors and the importance of maintaining high-quality social support for emotional well-being. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".