Prevalence and Correlates of Strength Exercise Among Breast, Prostate, and Colorectal Cancer Survivors
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To identify and compare the prevalence and correlates of strength exercise among breast, prostate, and colorectal cancer survivors. DESIGN: Cross-sectional, descriptive survey. SETTING: Nova Scotia, Canada. SAMPLE: 741 breast, prostate, and colorectal cancer survivors. . METHODS: A stratified sample of 2,063 breast, prostate, and colorectal cancer survivors diagnosed from 2003-2011 were identified and mailed a questionnaire. Descriptive, chi-square, and logistic regression analyses were used to determine any correlations among the main research variables. MAIN RESEARCH VARIABLES: Strength exercise behavior; medical, demographic, and motivational correlates using the Theory of Planned Behavior. FINDINGS: Of 741 respondents, 23% were meeting the strength exercise guidelines of two or more days per week. Cancer survivors were more likely to meet guidelines if they were younger, more educated, had a higher income, better perceived general health, fewer than two comorbidities, and a healthy body weight. In addition, those meeting guidelines had significantly more favorable affective attitude, instrumental attitude, injunctive norm, perceived behavioral control, planning, and intention. The correlates of strength exercise did not differ by cancer site. CONCLUSIONS: The prevalence of strength exercise is low among breast, prostate, and colorectal cancer survivors in Nova Scotia and the correlates are consistent across those survivor groups. . IMPLICATIONS FOR NURSING: Nurses should take an active role in promoting strength exercise among cancer survivors using the Theory of Planned Behavior, particularly among those survivors at higher risk of not performing strength exercise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".