Factors associated with depression and the relationship to quality of life among Saudi women with breast cancer
Bibliographic record
Abstract
Background and objective: Breast cancer is the most common malignancy among women worldwide, diagnosis and treatment and the months following primary therapy associated with different psychological symptoms that affect quality of life (QoL) in most women with breast cancer. The aim of this study was to identify the relationship between depression and quality of life QoL among Saudi women with breast cancer.Methods: Design: This is a descriptive, cross-sectional study. Methods: A convenience sample of 370 women with breast cancer was recruited from the out-clinic in King Faisal Specialist Hospital \& Research Center (KFSH&RC). Data were collected using self-report tools derived from the Patient-Reported Outcomes Measurement Information System (PROMIS) Emotional Distress-Depression-Short Form; the EORTC quality of life questionnaire (QLQ) (EORTC QLQ-C30, v.3.0 and EORTC QLQ-BR23); and from the Demographic and Clinical Data Sheet.Results: The correlation between depression and global health status with functioning subscales for the women was negative (Pearson’s r = -.357, p < .001; r = -.368, p < .001); whereas a positive correlation was found between depression and symptom subscales, together with its items.Conclusions: This study shows that depression is associated with QoL among Saudi women diagnosed with breast cancer. Therefore, depression affects upon QoL among the breast cancer population. However, enhancement of mental health statuses and psychological interventions helps to reduce the severity of depression and improves health related QoL among this population.
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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 | 0.002 |
| 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".