Project ECHO demonopolizes knowledge from expert specialists in academic centres to healthcare professionals in rural areas
Bibliographic record
Abstract
Physicians working in rural areas face challenges to keep up-to-date knowledge and skills, to form a community of practice to share resources, and to collaborate interprofessionally. Continuous medical education is available to doctors in the form of conferences, small group discussions, self-learning, and online accredited activities. However, these are usually not directly and immediately applicable to the complex problems that physicians encounter in practice. Project Extensions for Community Healthcare and Outcomes (ECHO) uses weekly sessions of videoconferences where an interprofessional group of specialists (the hub) is available to healthcare professionals working in rural, remote, and underserved areas (the spokes) to exchange knowledge. During these sessions, one member of the hub gives a short didactic presentation, followed by spokes who present their most challenging cases to the whole community for discussion and recommendations. ECHO was developed at the University of New Mexico for treatment of hepatitis C virus infection in 2004, and started in Canada for chronic pain and opioid stewardship in 2014. Research has shown that ECHO is effective in improving spokes’ knowledge and to increase access to specialist care in remote areas. ECHO has expanded to 150 partners in the United States, 14 in Ontario, and internationally to 33 countries.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".