Impact of simple conventional and Telehealth solutions on improving mental health in Afghanistan
Bibliographic record
Abstract
For more than a century Afghanistan has been unstable, facing decades of war, social problems, and intense poverty. As a result, many of the population suffer from a variety of mental health problems. The Government recognises the situation and has prioritised mental health, but progress is slow and services outside of Kabul remain poor. An international collaborative implemented a project in Badakshan province of Afghanistan using conventional and simple low-cost e-Health solutions to address the four most common issues: depression, psychosis, post-traumatic stress disorder, and substance abuse. Conventional town hall meetings informed community members to raise awareness and knowledge. In addition, an android-based mobile application used the World Health Organization's Mental Health Gap Action Programme guidelines and protocols to: collect information from community healthcare workers; provide referral services to patients; provide blended learning to improve providers' mental health knowledge, skills, and practice; and to provide store-and-forward and live consultations. Preliminary evaluation of the intervention shows enhanced access to care for remote communities, decreased stigma, and improved quality of health services. Primary care workers are also able to bridge the gap in consultations for rural and remote communities, connecting them with specialists and providing better access to care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".