Mood disturbance and depression in Arab women following hospitalisation from acute cardiac conditions: a cross-sectional study from Qatar
Bibliographic record
Abstract
OBJECTIVES: Depression is associated with increased morbidity and mortality rates among cardiovascular patients. Depressed patients have three times higher risk of death than those who are not. We sought to determine the presence of depressive symptoms, and whether gender and age are associated with depression among Arab patients hospitalised with cardiac conditions in a Middle Eastern country. SETTING: Using a non-probability convenient sampling technique, a cross-sectional survey was conducted with 1000 Arab patients ≥20 years who were admitted to cardiology units between 2013 and 2014 at the Heart Hospital in Qatar. Patients were interviewed 3 days after admission following the cardiac event. Surveys included demographic and clinical characteristics, and the Arabic version of the Beck Depression Inventory Second Edition (BDI-II). Depression was assessed by BDI-II clinical classification scale. RESULTS: 15% of the patients had mild mood disturbance and 5% had symptoms of clinical depression. Twice as many females than males suffered from mild mood disturbance and clinical depression symptoms, the majority of females were in the age group 50 years and above, whereas males were in the age group 40-49 years. χ(2) Tests and multivariate logistic regression analyses indicated that gender and age were statistically significantly related to depression (p<0.001 for all). CONCLUSIONS: Older Arab women are more likely to develop mood disturbance and depression after being hospitalised with acute cardiac condition. Gender and age differences approach, and routine screening for depression should be conducted with all cardiovascular patients, especially for females in the older age groups. Mental health counselling should be available for all cardiovascular patients who exhibit depressive symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".