Access to Care for Mental Health Problems in Afghanistan: A National Challenge
Bibliographic record
Abstract
BACKGROUND: This paper describes the access to care for mental health problems in Afghanistan, according to the nature of the mental health problems and the service provider. Following the Andersen model, it evaluates the respective roles in access to care of "predisposing," "needs," "enabling" factors, and other "environmental" factors such as exposure to traumatic events and level of danger of the place of residence. METHODS: Trans-sectional probability survey in general population by multistage sampling in 16 provinces, nationally representative: N=4445 (15 years or older), participation rate of 81%. Face to face interviews using standardized measures of mental health (CIDI, Composite International Diagnostic Interview). Different logistic regression models are presented. RESULTS: The 12-month rate of mental health help-seeking was 6.56% with substantial regional variation (2.35% to 12.65%). Providers were mainly from the health sector; the non-health sector (religious and healers) was also prevalent. Most consultations were held in private clinics (non-governmental organisation, NGO). The severity of mental health disorders as well as the perceived impairment due to mental health were independently very important: odds ratio (OR) = 6.04 for severe disorder, OR=3.79 for perceived impairment. Living in a dangerous area decreased access to care: for high level of danger and for very high level: OR=0.22. Gender, education and ethnicity were not associated with mental health help-seeking after controlling for exposure to trauma. CONCLUSION: Access to care for mental health problems depended mainly on the needs as defined as disorder severity level and impairment, and on environmental factors such as exposure to traumatic events. The system seems equitable; however, this is counterbalanced by a very challenging environment. This survey is a testimony to the hardship experienced by the Afghan population and by health professionals, and to the efforts to deliver organized mental healthcare in a challenging situation. This research may inform and support policy-makers and NGOs in other countries undergoing similar challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".