ANXIETY AND DEPRESSION AS KEY DETERMINANTS OF CANCER RELATED FATIGUE AMONG PATIENTS RECEIVING CHEMOTHERAPY
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
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.
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 | 0.001 | 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