The Role of Physical Activity in Cancer Survivors’ Quality of Life
Bibliographic record
Abstract
Abstract Purpose: As a result of a cancer diagnosis and treatment, many cancer survivors experience persistent physical, mental, and emotional symptoms that affect their quality of life. Physical activity has been identified as an intervention that may help to manage the side effects of a cancer diagnosis and its treatment. The purpose of this study was to investigate the role of physical activity on overall quality of life in adult cancer survivors. Methods: One-on-one semi structured interviews were conducted in person or via telephone with 13 adult (≥18 yrs) cancer survivors who had completed cancer treatment. Results: These cancer survivors described their physical activity as improving their physical functioning and mental health, as a means of positive social engagement, and adding positivity to their daily life. Conclusion: These results support the role of physical activity to enhance cancer survivors’ quality of life regardless of the individuals’ treatment(s) type, duration, or time since the end of active cancer treatment. Further research is warranted to (a) expand this research with a larger sample, (b) examine healthcare providers’ knowledge and application of exercise guidelines to cancer survivors in cancer care, and (c) explore implementation strategies for greater advocacy for healthcare providers to share the exercise recommendations with 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".