Predicting posttraumatic growth among breast cancer survivors: The role of social support, stress, and physical activity
Bibliographic record
Abstract
Breast cancer survivors (BCS) often experience stress that can have an ongoing impact on quality of life (Vivar & McQueen, 2005). Physical activity and social support have been identified as possible mechanisms to improve well-being among BCS (Courneya et al., 2002; Nausheen et al., 2009). Social support and cancer-related stress have also both been positively linked to posttraumatic growth (PTG), defined as positive psychological changes resulting from struggling with extremely challenging events (Tedeschi & Calhoun, 2004). The purpose of this study was to examine social support, stress, and physical activity as unique and combined predictors of PTG over time among BCS. Recently treated BCS (N = 162) completed measures of PTG at baseline (T1) and measures of social support, cancer-related stress, physical activity, and PTG 3 months later (T2). Participants ranged in age from 28-79 years, 85% were Caucasian, and 81% had at least some post-secondary education. Cancer worry (ß = .09) and social support in the form of understanding breast cancer (ß = .10) significantly predicted T2 PTG, while controlling for T1 PTG, F(3, 151) = 129.41, R2 = .71, p < .01, change in R2 = .02, p = .02. Physical activity was not directly linked to PTG, but social support and cancer appear to play a role in PTG for BCS. Physical activity contexts can be a source of social support for BCS (e.g., Sabiston et al., 2007), suggesting that activity may play a more complex role in PTG development.Acknowledgments: Research support from the Purdue Research Foundation and Canadian Institutes of Health Research/Canadian Breast Cancer Research Alliance
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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 | 0.001 | 0.001 |
| 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".