Cancer-related fatigue: the role of demographic and medical factors, symptom severity, illness perceptions, and coping strategies in testing Leventhal’s Common Sense Model in ovarian cancer patients
Bibliographic record
Abstract
Cancer-related fatigue (CRF) is the most common symptom among cancer patients. Up to 58% of ovarian cancer (OC) patients report debilitating fatigue. Yet, the risks for developing CRF remain poorly understood. The way patients’ perceive and cope with their symptoms may help to understand CRF. Leventhal’s Common Sense Model of Illness Perceptions was used to evaluate the effects of patients’ cancer-related perceptions on fatigue, using positive and negative coping strategies as mediators. OC patients (N = 283) completed self-report questionnaires. Results revealed that younger age, being unemployed, and greater anxiety, pain, nausea, and sleep dissatisfaction were associated with worse fatigue. Additionally, two illness perceptions, greater illness identity and consequences, were associated with worse fatigue. Indirect effect analyses revealed personal and treatment control to have an indirect effect on fatigue through positive coping. Implications for the CRF literature and relevance to OC patients are discussed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".