Cue to action: Are adults with a family history of cancer more likely to be physically active?
Bibliographic record
Abstract
The health benefits of moderate-to-vigorous intensity physical activity (MVPA) are well established. MVPA also plays a role in reducing adults' risk of developing certain cancers. Yet, few adults are sufficiently active. Based on the Health Belief Model (HBM), people who perceive themselves as being susceptible to health risks form intentions to take preventive actions. Whereas there is evidence that adults tend to have higher health risk perceptions when a family member is diagnosed with cancer, it is not clear if this predicts greater MVPA. Drawing on the HBM, we investigated the associations between perceived cancer severity, perceived cancer susceptibility, perceived PA benefits to reduce cancer risk, perceived PA barriers, and PA barrier self-efficacy with MVPA among adults with and without a family history of cancer. Data collected online from 493 adults (Mage=36 years) were analyzed using linear regression analysis controlling for body mass index, sex, and education. Perceived PA barriers (β=-.16), PA barrier self-efficacy (β=.31), and having a family member diagnosed with cancer were associated with MVPA (p<.05). Our findings do not support the notion that cancer risk perceptions are associated with MVPA, but do show that adults with family history of cancer engaged in more MVPA. Further, they corroborate findings from other studies that showed that adults who report less perceived PA barriers and greater PA barrier self-efficacy are more active, which may be taken as an argument for the inclusion of strategies to help adults' overcome perceived PA barriers and build PA barrier self-efficacy in MVPA 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".