Fatigue, Self-efficacy, Physical Activity, and Quality of Life in Women With Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: More than 192 000 US women faced the challenge of living with breast cancer in 2009. Although exercise may help combat treatment-related symptoms, cancer-related fatigue has been identified as a potential barrier to engaging in physical activity. Self-efficacy has been proposed to mediate the impact of cancer-related fatigue on physical activity and subsequently improve quality of life (QOL). OBJECTIVE: The purpose of this study was to determine the linkages among the concepts of an introductory model of fatigue related to cancer, self-efficacy for physical activity, physical activity, and QOL in women being treated for breast cancer. INTERVENTIONS/METHODS: Women currently receiving treatment for breast cancer were asked to complete 5 instruments: demographic profile, Piper Fatigue Scale, Physical Activity Assessment Inventory, Human Activity Profile, and McGill QOL Questionnaire. Structural equation modeling of the data was performed to determine the direct and indirect influences of study variables on QOL. RESULTS: The model was tested based on responses of 73 participants. All paths between variables were significant. The model explained 53% of the variance in QOL scores, 28% of the variance in physical activity, and 31% of the variance in self-efficacy. CONCLUSIONS: Although fatigue is most commonly thought of as a physical problem requiring physical intervention, this study provides emerging evidence to suggest there may be potential interventions to improve self-efficacy that may mediate the effect of fatigue on QOL. IMPLICATIONS FOR PRACTICE: Interventions to improve self-efficacy may contribute to increased physical activity and improved QOL in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it