eHealth for remote regions: Findings from Central Asia health systems strengthening project
Bibliographic record
Abstract
Isolated communities in remote regions of Afghanistan, Kyrgyz Republic, Pakistan and Tajikistan lack access to high-quality, low-cost health care services, forcing them to travel to distant parts of the country, bearing an unnecessary financial burden. The eHealth Programme under Central Asia Health Systems Strengthening (CAHSS) Project, a joint initiative between the Aga Khan Foundation, Canada and the Government of Canada, was initiated in 2013 with the aim to utilize Information and Communication Technologies to link health care institutions and providers with rural communities to provide comprehensive and coordinated care, helping minimize the barriers of distance and time. Under the CAHSS Project, access to low-cost, quality health care is provided through a regional hub and spoke teleconsultation network of government and nongovernment health facilities. In addition, capacity building initiatives are offered to health professionals. By 2017, the network is expected to connect seven Tier 1 tertiary care facilities with 14 Tier 2 secondary care facilities for teleconsultation and eLearning. From April 2013 to September 2014, 6140 teleconsultations have been provided across the project sites. Additionally, 52 new eLearning sessions have been developed and 2020 staff members have benefitted from eLearning sessions. Ethics and patient rights are respected during project implementation.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 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".