Analyzing the Prevalence of Depression and Anxiety Symptoms Among Relatives of Cancer Patients in Kuwait
Bibliographic record
Abstract
INTRODUCTION: The mental health impact on relatives of cancer patients frequently goes unnoticed and is commonly undervalued. This study aimed to explore how personal factors such as the patient's degree of kin, marital status, cancer stage, and number of diagnosed family members are correlated with the severity of depression and anxiety among relatives of cancer patients. METHOD: This self-administered cross-sectional survey was conducted in Kuwait, employing a random sampling method to recruit participants. Depression and anxiety symptoms were assessed using the validated Arabic versions of the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) scale. RESULTS: The mean age of the relatives of the cancer patients was 38.36 years (±13.44), with a female majority (59.72%). The prevalence of depression in the examined population was 60.1%, with the majority having mild depression (39.3%). On the other hand, the prevalence of anxiety in the same group was 51.2%, with the majority having mild disease (27.5%). Being female and having a cancer patient relative in the metastasis stage put patients' relatives at a greater risk of depression and anxiety. CONCLUSION: The diagnosis of cancer necessitates mental health screenings for patients' relatives, as findings from our study indicate that these individuals are at a high risk of developing depression and anxiety. Targeted support and referrals to specialists are crucial for mitigating the impact on their well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".