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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".