Ten Years of ECHO Chronic Pain and Opioid Stewardship in Ontario: Impact and Future Directions
Bibliographic record
Abstract
Background: ECHO Pain is a health professions education model that uses telehealth technology to connect specialists in academic centres to healthcare professionals who work in the community to disseminate best practice knowledge and foster interprofessional collaboration to support real patient cases. Methods: This paper summarizes 10 years of ECHO Pain implementation and evaluation in Ontario. We reviewed participants’ demographics, characteristics of cases presented in ECHO sessions, and the research output of this ECHO Pain program. Results: From June 2014 to June 2024, there were 529 sessions, 1527 healthcare professionals from urban and rural regions attended ECHO, and 25,898 h of continuing medical education credits were provided. We published 11 papers in peer-reviewed scientific journals using qualitative and quantitative research methods. Conclusions: ECHO Pain has been implemented and sustained in Ontario for 10 years, with demonstrated interprofessional education and an ongoing community of practice to discuss chronic pain cases. ECHO Pain is filling a significant gap in health professions education related to chronic pain in Ontario, especially for primary care professionals living in rural, remote, and underserved areas.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".