Relationship between Provider Communication Behaviors and the Quality of Life for Patients with Advanced Cancer in Saudi Arabia
Bibliographic record
Abstract
Context: Patients with advanced cancer from Saudi Arabia are often not well informed about diagnoses, prognoses, and treatment options. Poor communication can lead to health-care decisions that insufficiently meet patients’ preferences, concerns, and needs and that subsequently affect patients’ quality of life. Objectives: The purpose of this study is to examine the relationship between provider communication behaviors and the quality of life of patients with advanced cancer. Method: A cross-sectional, correlation design was used in the present study, in which 159 patients with confirmed diagnoses of stage III or IV solid cancer were surveyed. Results: The mean summary score of the patients’ quality of life was 57.31. We found a significant relationship between provider communication behaviors and patient quality of life (β = 0.18, b = 0.35, SE = 0.15, p = 0.021). In addition, R2 shows that only 3.4% of variance in patient quality of life is predicated on provider communication behaviors. Conclusions: The relationship between provider communication behaviors and patient quality of life was low (r = 0.18). A possible reason for this is that provider communication behaviors are not the only factor that affects patient quality of life; other variables, such as the patient’s age, cancer type, and level of awareness, can also have an effect. Another possible explanation is that communication behaviors between patients and providers may vary depending on the level of cultural contact.
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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 | 0.004 |
| 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".