Connecting women living beyond breast cancer for physical activity change: the importance of partner quality and social support on physical activity
Bibliographic record
Abstract
Support from a fellow person living beyond a cancer diagnosis may result in increased physical activity (PA), yet the quality of the partner match and perceived social support may impact the influence of partners on PA. This RCT examined group differences among inactive women living beyond a breast cancer diagnosis paired with another woman (MATCH; n = 54) versus paired with another woman and training with a qualified exercise professional (MATCHQEP; n = 54) on increasing PA over 10-weeks. Perceptions of partner match quality and amount of PA social support were compared. PA support was explored as a factor impacting change in PA. Post-intervention improvements in moderate-to-vigorous (MV)PA were observed in both groups, with MATCHQEP more active (d = .19). Perception of partner match quality was higher for the MATCH group compared to MATCHQEP (d = .75) whereas the MATCHQEP reported higher perceptions of PA support (d = .58). A multilevel linear model with group, PA-related support, and the interaction term predicting change in PA was tested. A significant interaction (b = 7.60, SE = 3.91, p = .05) was observed and simple slopes indicated women in the MATCHQEP who perceived greater PA support increased their PA additionally (b = 7.18, SE = 2.73, p = .01) compared to those who perceived lower levels of PA support (b = -.045, SE = 2.81, p = .87). PA support did not impact change in PA for the MATCH group. These findings have implications for peer-based behaviour change interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".