A national survey on depressive and anxiety disorders in Afghanistan: A highly traumatized population
Bibliographic record
Abstract
BACKGROUND: This survey attempts to measure at a national level, exposures to major traumas and the prevalence of common mental health disorders in a low-income dangerous country, highly affected by conflicts: Afghanistan. METHODS: Trans-sectional probability survey in general population by multistage sampling in 8 provinces, represented nationwide: 4445 adults (4433 weighted),15 years or older, 81% participation rate. Face to face interviews used specific scales for measuring lifetime exposure (LEC 5 Life Events Checklist) and Post Traumatic Stress Disorder (PTSD Check List), a diagnostic standardized interview: Composite International Diagnostic Interview (Short Form) for. Major Depressive Episode and Generalized Anxiety Disorder, plus scales for suicidal thoughts and attempts and psychological distress (MH5 and RE from SF36). RESULTS: 52.62% of the population is illiterate, 84,61% of the women do not have any source of income; 70.92% of the population lives in rural areas, 60.62% are below 35 years, 80% lives in very dangerous areas. 64.67% of the Afghan population had personally experienced at least one traumatic event; 78.48% had witnessed one such event. 60.77% of the sample experienced collective violence in relation to war and 48.76% reported four or more events; this very much differs across regions and levels of danger; women are less at risk for trauma except sexual violence, 35 years and above are more at risk than younger. 12-month PTSD prevalence reaches a high rate: 5.34% as MDE 11,71%, whereas GAD 2.78%; suicidal thoughts 2.26%, lifetime suicidal attempts 3.50% are close to reported in other countries. Women have more risk for PTSD (0R = 1.93) and suicidal behaviours (attempts OR = 1.92) than men; the number of events increases risk for MDE, PTSD and suicidal attempts, whereas education is protective. Exposure to different war events produced different mental health effects. People suffering from PTSD have higher risk to report 12-months suicidal ideations and lifetime suicidal attempts. CONCLUSION: Our findings highlight the need to map the extent and the types of mental disorders post conflict; this would help maximise the help to be offered in guiding proper choice of interventions, including education.
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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 | 0.002 |
| 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 | 0.001 |
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".