Post-traumatic growth influences physical activity within the first year following breast cancer treatment
Bibliographic record
Abstract
Post-traumatic growth (PTG) is a positive psychological change experienced as a result of adversity, such as breast cancer (BC). Positive emotions are closely associated with PTG. Based on the broaden and build theory, positive emotions broaden cognition and behaviour to promote the generation of and reception to a wide range of ideas and actions. Accordingly, PTG could foster participation in health enhancing moderate-to-vigorous physical activity (MVPA) among breast cancer survivors (BCS) coping with cancer-related trauma, however this relationship is not well understood. This study examined the association between PTG and change in MVPA over the first year following BC treatment by testing the hypothesis that BCS who experience PTG adopt effective and healthy coping strategies, including MVPA, to deal with cancer-related trauma. Women (n = 178) wore an accelerometer at two time points one year apart and completed the PTG survey at baseline. Residual change scores were calculated for MVPA. Preliminary findings show a significant decrease in MVPA over time, t(177) = 2.31, p =. 02 and moderate PTG scores. The PTG subscales of new possibilities and appreciation for life were significantly (p < 0.05) correlated with change in MVPA (r = .19 & .15, respectively). Both dimensions were also significant (p < 0.05) predictors of change in MVPA (R2 = .08 & .09) after controlling for cancer and personal characteristics. Feelings of new possibilities and appreciation for life may encourage new skills and greater value for one's life and priorities, leading to greater participation in MVPA following BC treatment.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".