Exercise Preferences of Endometrial Cancer Survivors
Bibliographic record
Abstract
Exercise has gained recognition as an effective supportive care intervention for cancer survivors, yet participation rates are low. Knowledge of the specific exercise counseling and programming preferences of cancer survivors may be useful for designing effective interventions. In this study, we examined the exercise preferences of 386 endometrial cancer survivors. Participants completed a questionnaire that included measures of past exercise behavior, exercise preferences, and medical and demographic information. Some key findings were as follows: (a) 76.9% of participants said they were interested or might be interested in doing an exercise program and (b) 81.7% felt they were able or likely able to actually do an exercise program. Participants also indicated that walking was their preferred activity (68.6%) and moderate exercise was their preferred intensity (61.1%). Logistic regression analyses showed that meeting public health guidelines for exercise, being overweight or obese, receiving adjuvant treatment, months since diagnosis, income, marital status, and level of education all influenced exercise preferences. These results suggest that endometrial cancer survivors have unique exercise preferences that are moderated by a number of demographic and medical variables. These findings may have implications for the design and implementation of clinical and population-based exercise interventions for endometrial cancer survivors.
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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 | 0.000 |
| 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".