Exploring health beliefs as predictors of moderate-to-vigorous intensity physical activity behaviour in cancer survivors
Bibliographic record
Abstract
Background: Regular participation in physical activity (PA) may help mitigate adverse side effects and symptoms associated with cancer treatments. It is recommended that cancer survivors engage in 150 minutes of moderate-to-vigorous intensity physical activity (MVPA) each week, but most do not follow these recommendations. Identifying theory-based factors associated with MVPA is important to understand how to promote PA engagement in this population. We examined whether Health Belief Model (HBM) constructs (i.e., perceived susceptibility of cancer, perceived severity of cancer, perceived benefits of PA for reducing cancer risk, perceived barriers to PA, PA barrier self-efficacy) were associated with self-reported MVPA behaviour in cancer survivors. Methods: 98 adult cancer survivors (Mage=48.9±15.2 years; 80.6% female) completed an online survey assessing sociodemographics, medical characteristics, MVPA behaviour, and HBM constructs. Data were analyzed using hierarchical linear regression analysis. Results: After adjusting for age, sex, body mass index, and time since cancer diagnosis, HBM constructs accounted for 31.5% of the variance in MVPA behaviour. Only perceived benefits of PA (B=7.96, SE=2.66, 95% confidence interval (CI): 2.75-13.18, p=.003) and PA barrier self-efficacy (B=0.44, SE=0.09, 95% CI: 0.26-0.63, p
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".