Posttraumatic growth in breast cancer survivors: The roles of physical activity and social support
Bibliographic record
Abstract
Posttraumatic growth (PTG) is the experience of positive psychological growth following adversity, such as a breast cancer diagnosis. There is preliminary theoretical evidence to suggest that physical activity may promote PTG in breast cancer survivors (BCS); however, causal mechanisms are not well defined. Social support is a theoretical predictor of PTG and may mediate the physical activity-PTG relationship, as physical activity contexts can create opportunities for social support. The aims of this study were to (i) test physical activity as a predictor of PTG and (ii) examine the mediating role of social support on this relationship. BCS (N=153, Mage=55, SD=11) completed self-reported measures assessing physical activity, social support and PTG at one-year post-primary treatment completion. Mediation models, controlling for ethnic background, body mass index, and marital status were estimated. There was a direct effect of physical activity on PTG (beta= .01, BCa CI=.002, .018) and an indirect effect through social support (beta= .001, BCa CI=.001, .004). Physical activity accounted for 9% of the variance in social support. Both physical activity and social support accounted for 15% of the variance in PTG. These findings demonstrate that obtaining social support and engaging in physical activity are associated with PTG in BCS, and that social support may be a mechanism by which physical activity fosters PTG. Further research is warranted to determine temporal causality through longitudinal and experimental designs. Improved understanding of the relationship between social support, physical activity and PTG will inform interventions to help BCS better cope with cancer.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".