Depression and anxiety symptoms among Afghan war widows and their associated factors: A cross-sectional analytical study
Bibliographic record
Abstract
Background: The 4 decades of conflict have particularly exacerbated the mental health of Afghan war widows, a population that has not been extensively studied in this context. Aim: This study aims to fill the gap in the literature by providing robust data on the prevalence of depression and anxiety symptoms and identifying associated factors among Afghan war widows. Methods: A cross-sectional study was carried out in 2023, interviewing war widows from four community health centers in Kandahar, Afghanistan. We employed a validated Patient Health Questionnaire and Generalized Anxiety Disorder Scale. A multivariable binary logistic regression model was used to determine factors associated with depression and anxiety symptoms. Results: The prevalence of depression and anxiety symptoms was 57.9% (95% CI: 52.7%-62.8%) and 61.5% (95% CI: 56.4%-66.4%), respectively. There were significant differences in the prevalence of mental health symptoms across our population with different sociodemographic and health-related profiles (in particular, time since widowhood, household income, history of comorbidity, and level of social support). Conclusion: The stark prevalence of mental health issues among Afghan war widows underscores an overlooked humanitarian crisis. The findings call for immediate mental health interventions, tailored to the sociopolitical realities of Afghanistan.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".