Exercise Preference Patterns, Resources, and Environment Among Rural Breast Cancer Survivors
Bibliographic record
Abstract
CONTEXT: Rural breast cancer survivors may be at increased risk for inadequate exercise participation. PURPOSE: To determine for rural breast cancer survivors: (1) exercise preference "patterns," (2) exercise resources and associated factors, and (3) exercise environment. METHODS: A mail survey was sent to rural breast cancer survivors identified through a state cancer registry, and 483 (30%) responded. FINDINGS: The majority (96%) were white, with mean education of 13 (+/-2.5) years and mean 39.0 (+/-21.5) months since diagnosis. Most participants (67%) preferred face-to-face counseling from an exercise specialist (27%) or other individual (40%). A third (31%) preferred home-based exercise with non face-to-face counseling from someone other than an exercise specialist. Participants preferring face-to-face counseling were more apt to prefer supervised exercise (38% vs 9%, P < 0.001) at a health club (32% vs 8%, P < 0.001). Home exercise equipment was reported by 63%, with 97% reporting home telephone and 67% reporting Internet access. Age, education, self-efficacy, treatment status, and exercise behavior were associated with exercise resources. The physical environment was often not conducive to exercise but a low crime rate and high trust in neighbors was reported. CONCLUSIONS: Rural health education programs encouraging exercise should offer multiple programming options while considering the physical environment and capitalizing on available resources and beneficial social environmental characteristics.
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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 | 0.000 | 0.000 |
| 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".