The effects of a walking intervention on self-efficacy for coping with cancer and quality of life among cancer patients during treatment
Bibliographic record
Abstract
Cancer affects millions of Americans each year and as such there are numerous individuals fighting this disease at any time. Though strides are being made every day to better treat and hopefully one day cure this disease, the needs of these patients is a current area receiving a high amount of attention (Meadows et al, 1998). There are numerous positive outcomes associated with physical activity including attenuating cancer side-effects. As such, exercise during cancer treatment has recently received attention within the literature.;The current study aimed to test a novel walking intervention utilizing pedometers to increase physical activity. In addition, the relationships between physical activity and self-efficacy for coping with cancer and quality of life were studied. Ten (N = 10) individuals were enrolled within the investigated. On average, individuals took 34,962.67 (SD = 10,635.49) steps per week throughout the six-week study. Due to the low number of individuals who completed the intervention, relationships between physical activity and the dependent variables are hard to quantify. However, when individuals were looked at in a single-subject fashion, a positive relationship between physical activity and psycho-social variables seemed to exist. Interviews were also conducted (n = 4) and themes of motivation and control arose from these interviews. Though the numerous limitations of the study prevented the use of adequate statistical techniques to quantify relationships among variables, the findings of this study point to the idea that increased physical activity is advantageous to cancer patients. Suggestions based on the numerous challenges of the current study are also included.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".