Factors Related to Persistent Fatigue Following Completion of Breast Cancer Treatment
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To verify the predictive capacity of the stress-process theory to emeanplain persistent fatigue following completion of breast cancer treatments; to verify the relationship between interleukin-1b and fatigue. DESIGN: Correlational. SETTING: Tertiary medical center in Quebec City, Canada. SAMPLE: A systematic sample of 103 women in remission from breast cancer was recruited. The mean age was 54 years. Participants with a depressive mood, insomnia, or stage IV cancer were emeancluded. METHODS: Participants were met during their follow-up appointment after the end of radiation therapy. Questionnaires on fatigue, stress variables, and other confounding variables were completed by telephone interview. Blood samples also were collected to measure the serum level of interleukin-1b. MAIN RESEARCH VARIABLES: Fatigue, several variables from the stress-process theory, pain, menopausal symptoms, and demographic and medical variables. FINDINGS: Fatigue was related theoretically and coherently to many stress-process variables. By controlling for pain, the final regression model included cancer stressors and passive and active coping as predictors, which accounted for 41% of the variance in fatigue. No relationship was found between fatigue and interleukin-1b. CONCLUSIONS: The results supported the relevance of the stress-process theory for emeanplaining cancer-related fatigue. IMPLICATIONS FOR NURSING: Nursing interventions based on this theoretical framework could be developed. In addition, further clinical research that tests the efficacy of these psycho-educative interventions in preventing persistent fatigue and improving the quality of life of women with breast cancer is recommended.
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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 | 0.007 |
| 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) | 0.002 | 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".