ANXIETY AND DEPRESSION AS KEY DETERMINANTS OF CANCER RELATED FATIGUE AMONG PATIENTS RECEIVING CHEMOTHERAPY
Bibliographic record
Abstract
Among non-communicable diseases, cancer is the second leading cause of death worldwide. In Jordan, it is the second leading cause of death. Fatigue is the most reported symptom among cancer patients. The purpose of this study was threefold: (1) to explore the prevalence of fatigue as a side effect of cancer chemotherapy (2) to examine the impact of chemotherapy on fatigue, and (3) to investigate psychological factors (depression and anxiety) that correlate with fatigue. A one group before and after quasi-experimental design was used to conduct this study. The Integrated Fatigue Model (IFM) was used to guide the study. A Convenience sampling technique was used to recriut78 participants diagnosed with cancer and treated with chemotherapy as the primary treatment. The sample was collected from two well-known Jordanian hospitals. Fatigue was measured using Piper Fatigue Scale (PFS) and the psychological variables (depression and anxiety) were measured using Hospital Anxiety and Depression Scale (HADS). Findings revealed an increase incidence of fatigue after a chemotherapy course. Also revealed was a statistically significant difference between pre and post chemotherapy fatigue mean total scores as well as behavioral, affective, sensory and cognitive dimensions. It was found that depression and anxiety have a positive relationship with fatigue. Depression explained 46% of fatigue score variance. Furthermore, anxiety explains 3.6% of the variance in fatigue scores. It could be concluded that fatigue is a prevalent symptom among cancer patients receiving chemotherapy. Depression and anxiety were identified as possible predictors of fatigue among cancer patients receiving chemotherapy.
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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 | 0.000 | 0.003 |
| 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".