Project ECHO demonopolizes knowledge from expert specialists in academic centres to healthcare professionals in rural areas Andrea D. Furlan (MD PhD)
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.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".