Physical activity and mental distress among Veteran cancer survivors with heart disease and diabetes
Bibliographic record
Abstract
Introduction: Mental distress frequently develops in sufferers of chronic diseases, including cancer. We examined the association between physical activity and mental distress among Veteran cancer survivors with heart disease or diabetes. Methods: Data for American military Veteran cancer survivors ( n = 16,643) were retrieved from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). Ordinary least squares (OLS) multiple linear regression was used to explore the effect of physical activity on self-reported mental distress among Veteran cancer survivors with coronary heart disease or diabetes. Geographic information system (GIS) software was used to examine the state-level distribution of mental distress among Veteran cancer survivors. Results: Geographic analysis showed that mental distress (in days) in the last month among Veteran cancer survivors was highest in Alabama (M = 3.99), while those in Wyoming reported the lowest number of days (M = 0.76). After controlling for relevant confounding factors, physical activity was associated with fewer days of mental distress days among Veteran cancer survivors with coronary heart disease. Discussion: Results of this study suggest that physical activity may be beneficial in reducing mental distress among Veteran cancer survivors with heart disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".