Exercise preferences among a population-based sample of non-Hodgkin's lymphoma survivors
Bibliographic record
Abstract
In the present study, we examined the exercise preferences of a population-based sample of non-Hodgkin's lymphoma (NHL) survivors. A secondary purpose was to explore the association between various demographic, medical, and exercise behaviour variables and elicited exercise preferences. Using a retrospective survey design, 431 NHL survivors residing in Alberta, Canada completed a mailed questionnaire designed to assess exercise preferences, past exercise behaviour, and various demographic variables. Overall, 77% of participants preferred or maybe preferred to receive exercise counselling at some point after their NHL diagnosis. An overwhelming majority indicated that they would possibly be interested (81%) and able (85%) to participate in an exercise programme designed for NHL survivors. The majority of participants (55%) listed walking as their preferred choice of exercise. Logistic regression analyses indicated that NHL survivors' exercise preferences were influenced by body mass index (BMI), exercise behaviour, and gender. Eliciting exercise preferences from the population in question yields important information for cancer care professionals designing exercise programmes for NHL survivors. Furthermore, tailoring exercise programmes to the preferences of NHL survivors may be one method to potentially enhance exercise adherence in this population both inside and outside of clinical trials.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".