Correlates of Cancer-Related Fatigue among Colorectal Cancer Patients Undergoing Postoperative Adjuvant Therapy Based on the Theory of Unpleasant Symptoms
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue (CRF) is a common and burdensome symptom in cancer patients that is influenced by multiple factors. Identifying factors associated with CRF may help in developing tailored interventions for fatigue management. This study aimed to examine the correlates of CRF among colorectal cancer patients undergoing postoperative adjuvant therapy based on the theory of unpleasant symptoms. METHODS: A cross-sectional study was implemented, and finally, a sample of 363 participants from one tertiary general hospital and one tertiary cancer hospital was purposively recruited. Data were collected using the general information questionnaire, cancer fatigue scale, the distress disclosure index, Herth hope index, and perceived social support scale. Univariate analysis and multiple linear regression analysis were performed to determine the correlates of CRF. RESULTS: The mean score of CRF among colorectal cancer patients was 21.61 (SD = 6.16, 95% CI 20.98-22.25), and the fatigue degree rating was "moderate". The multiple linear regression model revealed that 49.1% of the variance in CRF was explained by hope, sleep disorder, internal family support, self-disclosure, pain, and time since operation. CONCLUSIONS: Our study identified several significant, modifiable factors (self-disclosure, hope, internal family support, pain, and sleep disorder) associated with CRF. Understanding these correlates and developing targeted psychosocial interventions may be associated with the improvement of CRF in patients with colorectal cancer.
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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 | 0.004 |
| 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) | 0.001 | 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".